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+# Lab-P13: SQL Databases and Plotting
+
+In this lab, you'll learn to use SQL queries to extract data from a database. You will also write various plotting functions to visualize the extracted data.
+
+-----------------------------
+## Corrections/Clarifications
+
+
+**Find any issues?** Please report to us:
+
+- Ashwin Maran <amaran@wisc.edu>
+
+## Learning Objectives:
+
+## Learning Objectives
+
+In this lab, you will practice how to:
+
+* use SQL queries to extract data from a database,
+* use different SQL keywords to organize data,
+* write different plotting functions to visualize data and engage in data exploration.
+
+------------------------------
+
+## Note on Academic Misconduct
+
+You may do these lab exercises only with your project partner; you are not allowed to start
+working on Lab-P13 with one person, then do the project with a different partner. Now may be a
+good time to review [our course policies](https://cs220.cs.wisc.edu/f23/syllabus.html).
+
+**Important:** P12 and P13 are two parts of the same data analysis.
+You **cannot** switch project partners between these two projects.
+If you partnered up with someone for P12, you have to sustain that partnership until the end of P13.
+
+------------------------------
+
+## Segment 0: Setup
+
+Unlike previous labs, you will **not** be working on an Otter notebook in this lab. Most importantly, this means that `public_tests.py` will **not** be provided to you. There will be `assert` statements in your `lab-p13.ipynb` notebook to guide you, but they will **not** be comprehensive. Instead, if you come across any syntactical or semantic errors, you will have to debug your code by yourself. Feel free to reach out to your TA or PM if you get stuck anywhere. and you will instead learn how to test your code by yourself.
+
+You **will** however be provided with a `public_tests.py` for the project.
+
+First, create a `lab-p13` directory and download the `lab-p13.ipynb` file into the directory.
+
+## Segments 1-3: Web Requests, Caching, DataFrames and Scraping
+
+For the remaining segments, detailed instructions are provided in `lab-p13.ipynb`. From the terminal, open a `jupyter notebook` session, open your `lab-p13.ipynb` and follow the instructions in `lab-p13.ipynb`.
+
+## Project 13
+
+You can now get started with [P13](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p13). **You may copy/paste any code created here in project P13**. Remember to only work on P13 with your partner from this point on. Have fun!
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+# Images
+
+Images from lab-p13 are stored here.
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "115889c5",
+   "metadata": {},
+   "source": [
+    "# Lab-P13: Analyzing World Data with SQL\n",
+    "\n",
+    "In this lab, you will practice how to:\n",
+    "\n",
+    "* write SQL queries,\n",
+    "* create your own plots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daed65a3",
+   "metadata": {},
+   "source": [
+    "# Segment 1: Setup\n",
+    "\n",
+    "### Task 1.1: Import the required modules\n",
+    "\n",
+    "We will first import some important modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e59b7bdb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n",
+    "\n",
+    "import sqlite3\n",
+    "import pandas as pd\n",
+    "import matplotlib\n",
+    "import math\n",
+    "import numpy as np # this is *only* for the function get_regression_coeff - do NOT use this module elsewhere"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97a3f1e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this ensures that font.size setting remains uniform\n",
+    "%matplotlib inline \n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "matplotlib.rcParams[\"font.size\"] = 13 # don't use value > 13! Otherwise your y-axis tick labels will be different."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75adca21",
+   "metadata": {},
+   "source": [
+    "### Task 1.2: Use the `download` function to download `rankings.json`\n",
+    "\n",
+    "**Warning:** For the lab and the project, do **not** download the dataset `rankings.json` manually (you **must** write Python code to download this, as in P12). When we run the autograder, this file `rankings.json` will not be in the directory. So, unless your `p13.ipynb` downloads this file, you will get a **zero score** on the project. Also, make sure your `download` function includes code to check if the file already exists. The Gradescope autograder will **deduct points** otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bb742ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# copy the definition of your 'download' function from P12 here - remember to import the necessary modules\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe96e53b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/raw/main/p13/rankings.json'\n",
+    "# to the file 'rankings.json'\n",
+    "\n",
+    "download(\"https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/raw/main/p13/rankings.json\", \"rankings.json\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0023581a",
+   "metadata": {},
+   "source": [
+    "### Task 1.3: Create a database called 'rankings.db' out of 'rankings.json'\n",
+    "\n",
+    "You can review the relevant lecture code here: [Mike](https://canvas.wisc.edu/courses/374263/files/folder/Mikes_Lecture_Notes/Lec32_Database_1), [Gurmail](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Gurmail_Lecture_Notes/32_Database-1), and [Cole](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Cole_Lecture_Notes/32_Database1)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "270d8da5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a database called 'rankings.db' out of 'rankings.json'\n",
+    "\n",
+    "# TODO: load the data from 'rankings.json' into a variable called 'rankings' using pandas' 'read_json' function\n",
+    "# TODO: connect to 'rankings.db' and save it to a variable called 'conn'\n",
+    "\n",
+    "# write the contents of 'rankings' to the table 'rankings' in the database\n",
+    "# we have done this one for you\n",
+    "rankings.to_sql(\"rankings\", conn, if_exists=\"replace\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84a77c79",
+   "metadata": {},
+   "source": [
+    "### Task 1.4: Read all the rows in rankings (the database table)\n",
+    "\n",
+    "You'll have to use pandas's `read_sql` function to make a query."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a300adde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rankings', display its head\n",
+    "# remember to display ONLY the head and NOT the whole DataFrame\n",
+    "# replace the ... with your code\n",
+    "\n",
+    "rankings = pd.read_sql(\"SELECT ... FROM ...\", conn)\n",
+    "rankings.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e4d16ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert len(rankings) == 1500\n",
+    "assert rankings.iloc[0][\"Country\"] == \"United States\"\n",
+    "assert rankings.iloc[-1][\"Institution Name\"] == \"Ewha Womans University\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b09ee5a",
+   "metadata": {},
+   "source": [
+    "# Segment 2: SQL Practice\n",
+    "\n",
+    "In practice, we often are more interested in writing more specific queries about our data. For example, we might be interested in finding institutions in the *United States*, or data collected in the `Year` *2022*, or both. With SQL, **WHERE** and **AND** clauses can help filter the data accordingly.\n",
+    "\n",
+    "Before proceeding with this segment, it is **recommended** that you **review** the relevant lecture code here:\n",
+    "1. [Mike](https://canvas.wisc.edu/courses/374263/files/folder/Mikes_Lecture_Notes/Lec33_Database_2), [Gurmail](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Gurmail_Lecture_Notes/33_Database-2), and [Cole](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Cole_Lecture_Notes/33_Database2),\n",
+    "\n",
+    "2. [Mike](https://canvas.wisc.edu/courses/374263/files/folder/Mikes_Lecture_Notes/Lec36_Database_3), [Gurmail](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Gurmail_Lecture_Notes/36N_Database-3), and [Cole](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Cole_Lecture_Notes/36_Database3)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cebe083",
+   "metadata": {},
+   "source": [
+    "### Task 2.1: Use `WHERE` to find institutions in the United States\n",
+    "\n",
+    "* Write a query to select the rows from the database with *United States* as the `Country`.\n",
+    "* Keep only the `Institution Name` column.\n",
+    "* Save these institution names to a **list**.\n",
+    "\n",
+    "**Hint:** You will need to use **quotes** (`'`) around the **strings** in your query and **backticks** (``` ` ```) around **column names** as in the example below. The **quotes** and **backticks** are only **required** when the string or column name contains special characters or spaces. But even otherwise, it is a good idea to use them to be on the safe side.\n",
+    "\n",
+    "In this case, the columns ``` `Institution Name` ``` and ``` `Country` ``` are columns, and need to be inside **backticks**, whereas `'United States'` is a string in the query, and needs to be inside **quotes**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64012949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "us_institutions_df = pd.read_sql(\"SELECT `Institution Name` FROM rankings WHERE `Country` = 'United States'\", conn)\n",
+    "us_institutions = list(us_institutions_df['Institution Name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c035f899",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert \"University of Wisconsin-Madison\" in us_institutions\n",
+    "assert \"Tampere University\" in list(rankings[\"Institution Name\"])\n",
+    "assert \"Tampere University\" not in us_institutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fe4da4e",
+   "metadata": {},
+   "source": [
+    "### Task 2.2: Add an `AND` clause to find institutions in the United States with at least 70 `Overall`\n",
+    "\n",
+    "* Copy your query from Task 2.1.\n",
+    "* Update it to only select rows with `Overall` of **at least** *70*.\n",
+    "\n",
+    "**Hint:** You will need to use **quotes** (`'`) around the **strings** in your query and **backticks** (``` ` ```) around **column names**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12f341ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'good_us_institutions', but do NOT display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25e2d3cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert \"Massachusetts Institute of Technology (MIT) \" in good_us_institutions\n",
+    "assert \"University of Texas at Austin\" in good_us_institutions\n",
+    "assert \"University of Wisconsin-Madison\" not in good_us_institutions\n",
+    "assert \"University of Connecticut\" not in good_us_institutions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf715227",
+   "metadata": {},
+   "source": [
+    "### Task 2.3: Use an `ORDER BY` clause to display the top 7 institutions by `Academic Reputation` in 2022\n",
+    "\n",
+    "In addition to **WHERE** and **AND**, the **ORDER BY** keyword helps organize data even further. Much like the `sort_values()` function in `pandas`, the **ORDER BY** clause can be used to organize the result of the query in *increasing* (**ASC**) or *decreasing* (**DESC**) order based on a column's values.\n",
+    "\n",
+    "* Write a new query to select rows in rankings where the `Year` is *2022*.\n",
+    "* Use **ORDER BY** and **LIMIT** to select the top 7 rows with the **highest** `Academic Reputation`.\n",
+    "* Save these institution names to a **list**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "763304e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_7_institutions', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "404fa832",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert len(top_7_institutions) == 7\n",
+    "assert top_7_institutions[0] == \"Massachusetts Institute of Technology (MIT) \"\n",
+    "assert top_7_institutions[-1] == \"University of California, Berkeley (UCB)\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "13e1803b",
+   "metadata": {},
+   "source": [
+    "### Task 2.4: Order by multiple columns\n",
+    "\n",
+    "If you print out the resulting dataframe from your query, you might notice that all 7 rows have the same academic reputation. This makes it hard to compare the universities, so we will add some **tiebreaking** rules. If two universities have the same `Academic Reputation`, then we should order them by their `Citations per Faculty` (with the **highest** appearing first). You can do this by ordering by multiple columns.\n",
+    "\n",
+    "* Copy your query from Task 2.3.\n",
+    "* Update the **ORDER BY** clause to add this tiebreaking behavior.\n",
+    "* Save these institution names to a **list**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26f5a433",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_7_with_tiebreak', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5b2382b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert top_7_with_tiebreak[0] == \"Massachusetts Institute of Technology (MIT) \"\n",
+    "assert top_7_with_tiebreak[-1] == \"University of California, Los Angeles (UCLA)\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b991dcf",
+   "metadata": {},
+   "source": [
+    "### Task 2.5: Use `GROUP BY` clause and `SUM` aggregate function to get the total number of `International Students` for each `Country` in 2022.\n",
+    "\n",
+    "The **GROUP BY** keyword groups rows that have the same value. It is often used with aggregate functions, such as **COUNT**, **SUM**, **AVG**, etc. to obtain a summary about groups in the data.\n",
+    "\n",
+    "For example, to answer the question \"What is the average rank of each country's institutions?\", we could **GROUP BY** the `Country` and use the **AVG** aggregate function on the `Rank` column in the **SELECT** clause to get the average rank of each country.\n",
+    "\n",
+    "* Write a new query that uses **GROUP BY** and **SUM** to get the total number of international students in each country, using **WHERE** to filter by the `Year`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `Country` and the **sum** of the `International Students` for that country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f31786c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'inter_students_by_country', then display its head\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c84f12c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert math.isclose(inter_students_by_country[inter_students_by_country[\"Country\"] == \"Japan\"].iloc[0][1], 308.6)\n",
+    "assert math.isclose(inter_students_by_country[inter_students_by_country[\"Country\"] == \"Australia\"].iloc[0][1], 2108.0)\n",
+    "assert math.isclose(inter_students_by_country[inter_students_by_country[\"Country\"] == \"United States\"].iloc[0][1], 3706.7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06ecba29",
+   "metadata": {},
+   "source": [
+    "### Task 2.6: Use the `AS` keyword to rename the new column from Task 2.5 to `Total International Students`\n",
+    "\n",
+    "Although the dataframe does have a column for the sum of international students for each country, the name of the column looks strange:\n",
+    "\n",
+    "```sql\n",
+    "SUM(`International Students`)\n",
+    "```\n",
+    "\n",
+    "In SQL, the **AS** keyword allows us to create an simpler alias for the columns we create with our queries to make the resulting **DataFrame** easier to understand.\n",
+    "\n",
+    "* Paste your query from Task 2.5 and modify it so the **SUM** column has the name `Total International Students`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `Country` and `Total International Students`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3947be0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'inter_students_by_country_renamed', then display its head\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e114959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert \"Total International Students\" in inter_students_by_country_renamed.columns\n",
+    "assert math.isclose(inter_students_by_country_renamed[inter_students_by_country_renamed[\"Country\"] == \"Japan\"][\"Total International Students\"], 308.6)\n",
+    "assert math.isclose(inter_students_by_country_renamed[inter_students_by_country_renamed[\"Country\"] == \"Australia\"][\"Total International Students\"], 2108.0)\n",
+    "assert math.isclose(inter_students_by_country_renamed[inter_students_by_country_renamed[\"Country\"] == \"United States\"][\"Total International Students\"], 3706.7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79fdda0c",
+   "metadata": {},
+   "source": [
+    "### Task 2.7: Use the `HAVING` keyword to only keep countries with more than 1000 `Total International Students`\n",
+    "\n",
+    "In addition to **WHERE**, the **HAVING** keyword is useful for filtering **GROUP BY** queries. Whereas **WHERE** filters the number of rows, **HAVING** filters the number of groups.\n",
+    "\n",
+    "* Paste your query from Task 2.6 and modify it so that it only returns countries (`Country`) and `Total International Students` with **more than** *1000* `Total International Students`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `Country` and `Total International Students`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bc00cf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'inter_students_by_country_more_than_1000', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1c5be56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to confirm that your variable has been defined properly\n",
+    "\n",
+    "assert len(inter_students_by_country_more_than_1000) == 6\n",
+    "assert \"Australia\" in list(inter_students_by_country_more_than_1000[\"Country\"])\n",
+    "assert \"Germany\" in list(inter_students_by_country_more_than_1000[\"Country\"])\n",
+    "assert \"United Kingdom\" in list(inter_students_by_country_more_than_1000[\"Country\"])\n",
+    "assert \"United States\" in list(inter_students_by_country_more_than_1000[\"Country\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d83309db",
+   "metadata": {},
+   "source": [
+    "# Segment 3: Plotting\n",
+    "\n",
+    "SQL provides powerful tools to manipulate and organize data. Now we might be interested in plotting the data to engage in data exploration and visualize our results.\n",
+    "\n",
+    "Before starting this segment, it is recommended that you go through the relevant lecture code here:\n",
+    "\n",
+    "1. [Mike](https://canvas.wisc.edu/courses/374263/files/folder/Mikes_Lecture_Notes/Lec34_Plotting_1), [Gurmail](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Gurmail_Lecture_Notes/34N_Plotting-1), and [Cole](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Cole_Lecture_Notes/34_Plotting1),\n",
+    "\n",
+    "2. [Mike](https://canvas.wisc.edu/courses/374263/files/folder/Mikes_Lecture_Notes/Lec35_Plotting_2), [Gurmail](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Gurmail_Lecture_Notes/35N_Plotting-2), and [Cole](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-lecture-material/-/tree/main/f23/Cole_Lecture_Notes/35_Plotting2)."
+   ]
+  },
+  {
+   "attachments": {
+    "bar_plot.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "37d209ea",
+   "metadata": {},
+   "source": [
+    "### Task 3.1: Use a bar plot to plot the data from Task 2.7\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:bar_plot.jpg\" width=\"400\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e4dc5d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instead of specifically plotting just the DataFrame 'inter_students_by_country_more_than_1000',\n",
+    "# create a general function to create bar plots\n",
+    "\n",
+    "def bar_plot(df, x, y):\n",
+    "    \"\"\"bar_plot(df, x, y) takes in a DataFrame 'df' and displays \n",
+    "    a bar plot with the column 'x' as the x-axis, and the column\n",
+    "    'y' as the y-axis\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: use df.plot.bar to plot the data in black with no legend\n",
+    "    # TODO: set x as the x label \n",
+    "    # TODO: set y as the y label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e21ed94a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell to plot the data from Task 2.7\n",
+    "# verify that this plot matches exactly with the image shown above\n",
+    "\n",
+    "bar_plot(inter_students_by_country_more_than_1000, 'Country', 'Total International Students')"
+   ]
+  },
+  {
+   "attachments": {
+    "scatter_plot.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "e44a467e",
+   "metadata": {},
+   "source": [
+    "### Task 3.2: Use a scatter plot to plot the relationship between `Employer Reputation` and `Academic Reputation` in 2022\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:scatter_plot.jpg\" width=\"400\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69f4605a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create a general function to create scatter plots\n",
+    "\n",
+    "def scatter_plot(df, x, y):\n",
+    "    \"\"\"scatter_plot(df, x, y) takes in a DataFrame 'df' and displays \n",
+    "    a scatter plot with the column 'x' as the x-axis, and the column\n",
+    "    'y' as the y-axis\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: use df.plot.scatter to plot the data in black with no legend\n",
+    "    # TODO: set x as the x label \n",
+    "    # TODO: set y as the y label"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d77b0f09",
+   "metadata": {},
+   "source": [
+    "With the `scatter_plot` function defined, you are ready to create the required plot.\n",
+    "\n",
+    "* Write a SQL query to select rows from the database where the `Year` is *2022*.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `Employer Reputation` and `Academic Reputation`.\n",
+    "* Call `scatter_plot`, passing in `Employer Reputation` and `Academic Reputation` as the `x` and `y` arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ef617ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame\n",
+    "# then create the scatter plot using the DataFrame\n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "attachments": {
+    "horizontal_bar_plot.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "416b8734",
+   "metadata": {},
+   "source": [
+    "### Task 3.3: Make a Horizontal Bar plot of average `Employer Reputation` and average `Faculty Student` across all years\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:horizontal_bar_plot.jpg\" width=\"600\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78e21b0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "def horizontal_bar_plot(df, x):\n",
+    "    \"\"\"horizontal_bar_plot(df, x) takes in a DataFrame 'df' and displays \n",
+    "    a horizontal bar plot with the column 'x' as the x-axis, and all\n",
+    "    other columns of 'df' on the y-axis\"\"\"\n",
+    "    df = df.set_index(x)\n",
+    "    ax = df.plot.barh()\n",
+    "    ax.legend(loc='center left', bbox_to_anchor=(1, 0.9))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cbdaa9f",
+   "metadata": {},
+   "source": [
+    "Use the `horizontal_bar_plot` function to create the required plot.\n",
+    "\n",
+    "* Write a SQL query to select `Year`, **average** `Employer Reputation`, and **average** `Faculty Student` grouped by `Year`.\n",
+    "* Save the resulting **DataFrame** with **three** columns: `Year`, the **average** of the `Employer Reputation` and the **average** of the `Faculty Student`.\n",
+    "* Call `horizontal_bar_plot`, passing in `Year` as the `x` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc779e0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame\n",
+    "# then create the horizontal bar plot using the DataFrame\n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "attachments": {
+    "pie_plot.jpg": {
+     "image/jpeg": 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8PPkbbdFvP4VzRYq7xv327Vu1Y+CSnZ0dTExMkJubix07dqh8ky1Jq1at5JdjcnJysHPnTsyePRuffvophg8frlbRs44dO0IoFOLSpUt4+/YtXr58ialTp8pv79KlCw4fPoyYmBhcuXIFJiYmaNeuXdkeqB6xt7eHsbExJBIJIiMjVY68vH79GgBUjpAkJycjLS0NlpaWSrcVzv3Q1EidKvXq1YNYLEZiYiLevn2rNAogkUjklxiaNWumVp/9+vXD5s2b8ddffyEuLq7EuSAPHjyQL7vt06ePwm0ikQgjR47Eli1bcPjwYcyfPx8HDx4EUDA68j5nZ2c8e/YMM2fO1Oio2p9//ons7GwMGTJEPo+sqJcvX2rsXETzaA4IUVudOnXkyy1nzJhR6l4vRa+Pd+rUCUDBcKmqZOLw4cPIzc2Fh4eHwpt64f8/f/5c6T5Pnz4t8dtheRQmPJreB8PIyAjdu3dHXl4eAgMDK9SXWCzGzJkz4enpCYlEovb1bXNzc7Ru3Rrx8fHyKrVFRzh8fX3BGMOaNWuQlpaG9u3bq50oaet5q8h5jYyM8MEHHwCA/MOxqPz8fPz0008A/pt/877CSwpFRUZG4sqVK+DxeOjQoUO5Yy+NsbExevbsCQAql5IGBARAIpGgRYsWxV7Wel+PHj3QtGlTSKVSzJo1q9h2eXl5mDlzJoCC+RdeXl5KbQoTjYMHDyI9PR0BAQEQi8UqS84Xzvt5v/5ORSUlJQFQfVkvISEB58+f1+j5iGZRAkLKZOvWrXBxccGVK1fQs2dPlYlBQkICFi5ciC5dusiPderUCS1btkRycjJmzpypkLy8ePECX331FYCCFSpFFX5AbtmyBTExMfLj0dHRGD9+fKmXYMqqMOF5+vSpRvsFgMWLF0MoFGL69OkICAhQuj03NxcnT57E9evX5ce2bt2qcj+dhw8f4s2bN+Dz+fLRJXUUfvvctm0beDyewu+o6G1A2S6/FG5mFhsbi+TkZLXvV1Gl/b7mzJkDAPj+++8VLh3KZDIsWrQIL168gLOzc7GTI7/55huF5z8nJwczZsyAVCrFwIED1b70UV4LFiwAAKxYsUI+2gEUXHoovO3zzz9Xuz8ej4eDBw/CxMQER48exfjx45GamqrQJjY2FoMHD8bly5dRo0YNbN26VWVfrVu3RoMGDfDo0SN8+eWX8to+qkbjJk+eDGdnZ2zfvh2rVq1SOc/n5s2bxa5GKk7hxOXjx48jNjZWfjwzMxOTJk0qcV4I0QPcrP4lldm7d+9Y69at5XU2mjRpwoYOHcqGDx/OvL29mUAgYABY3bp1Fe73/Plzeb0PZ2dnNmLECNa7d29mbGzMALBRo0YxmUymcB+JRCKvK2Bra8sGDBjAunbtyiwsLJiPjw/74IMPSqwDUtb6IT/88IO8HsWQIUPYxIkT2cSJE1lCQgJjrPgaH0WV1ObIkSNMLBbL65D07duXDR06lHl7ezMrKysGgG3btk3evvCxe3p6skGDBrHRo0ezzp07MyMjIwaAzZ8/v9g4VCmsP4Fi6kt4eHjIb79x44bS7SXV+/Dz85PfNnr0aDZx4kT2+eefy28vrANSXD2H0m5XpbTfF2OMzZ07lwFgAoGA+fr6slGjRjEvLy8GgFlbW7Pr16+rfIwuLi5s4MCBTCwWsz59+rDhw4ezGjVqyB9jZGSk2nEyxti0adOYt7c38/b2ZvXr12cAmJmZmfyYt7c3+/PPP5Xut3jxYgaAiUQi1qdPHzZo0CBmbm5eoZo8//77L6tevToDwExMTFj37t3ZqFGjmI+PDxOJRAwAq1+/PgsLCyuxn1WrVslfL3ivZsz7Hjx4wJydnRkA5uDgwLp27cpGjBjBOnXqJH9fGDFihMJ9Svs7lkql8r8RS0tLNmDAADZ48GBmb2/PHB0d5XVM1K33QXVAdIsSEFIuMpmMHTt2jA0bNoy5uLgwExMTZmxszGrXrs2GDh3Kfv31V5aXl6d0v9jYWDZnzhzm6enJRCIRs7CwYB06dGD79+9XSj4KxcfHs8mTJ7Pq1aszkUjEPDw82OLFi1lOTk6phcjKmoDk5+ezb7/9ltWrV0+eGKFIoauKJiCMMfbixQs2Y8YM5uXlxUxMTJiZmRnz9PRk/fv3Zzt37mSJiYnytidPnmRTpkxhTZs2ZXZ2dvLnuF+/fiW+2RcnJyeHmZiYMADss88+U7p9ypQp8jdzVb+/khKQhIQENnHiRFarVi15glS0nTYSkNJ+X4VOnDjBunXrxqytrZlQKGQuLi5sypQpKguYFX2MUqmULVu2jNWpU4eJRCJWvXp1NnnyZBYTE6N2jIWKFtUq7qe4x378+HHWsWNHZmFhwczMzFirVq3Y7t27yxxDURkZGWzt2rWsQ4cOzM7OjgmFQubg4MC6d+/Odu7cyaRSaal9REREMD6fLy/apuo1U1RycjL79ttvWatWrZiFhQUzNjZmLi4urFOnTmzFihXs5cuXCu1L+ztmjLGUlBT5e4qxsTGrWbMmmzBhAouIiChzokEJiG7xGKPpwYQQUig8PBxubm6oXbu20p4yhBDNoTkghBBCCNE5SkAIIYQQonOUgBBCCCFE52gOCCGEEEJ0jkZACCGEEKJzlIAQQgghROcoASGEEEKIzlECQgghhBCdowSEEEIIITpHCQghhBBCdI4SEEIIIYToHCUghBBCCNE5SkAIIYQQonOUgBBCCCFE5ygBIYQQQojOUQJCCCGEEJ2jBIQQQgghOkcJCCGEEEJ0jhIQQgghhOgcJSCEEEII0TlKQAghhBCic5SAEEIIIUTnKAEhhBBCiM5RAkIIIYQQnaMEhBBCCCE6RwkIIYQQQnSOEhBCCCGE6BwlIIQQQgjROUpACCFEwy5dugQej4dVq1YV28bc3Bw+Pj66C0oDeDweevXqpXQ8JiYGTZs2hZGREfbt28dBZGUXHh6OqVOnol69ejA1NYWNjQ0aNGiAcePGISgoqNj7nTt3DjweD3w+H69evSq2bx6Pp/BjYmKChg0bYsmSJcjKytLWw6pUjLgOgBBCSOUVHh6O7t274927d/jtt9/g5+fHdUilunPnDjp37gyhUIixY8eifv36yM7ORmhoKE6ePAkLCwt06dJF5X337NkDZ2dnxMfHY9++ffj222+LPY+vry/Gjx8PAIiPj8fx48fxzTff4MaNG/jrr7+08tgqE0pACCGElMuTJ0/Qo0cPpKam4tSpU+jatSvXIall6dKlyMjIwP3799GkSROF2zZv3oyYmBiV90tMTERgYCAWLlyIp0+fYv/+/Vi2bBn4fNUXE+rUqYOPPvpI/u9Zs2bB29sb586dQ0hICFq2bKm5B1UJ0SUYQgjRIydPnkTHjh1hYWEBMzMztGnTBkeOHFFq5+PjA1dXV0RERGD48OGwsbGBmZkZevbsidDQUKX27969w4gRI2BtbQ1zc3P4+vrizp078n7K6vbt2+jUqRNycnJw4cIFpeQjKysLixYtQp06dWBsbAwHBweMGDFCKbbCyxVLly5FQEAAWrZsCbFYjBo1amD+/PnIy8tTOndgYCBatGghbzdz5kw8fvxY3k9pQkNDYWdnp5R8AACfz4eTk5PK+/3000+QSqUYN24c/P39ERERUaaRDD6fL7/s9uLFC7XvZ6goASGEEC3JyspCQkKCyh9Vdu7ciQEDBiA2NhZffPEFli1bBqlUitGjR2PFihVK7TMzM9G5c2eIRCKsWLECM2bMwKVLlzBw4EDk5+fL2yUnJ6NDhw44fvw4Ro8eje+//x4eHh7o2rUrIiMjy/y4Ll68CF9fX4jFYly5cgVt2rRRuD0vLw+9e/fG8uXL0bhxY6xfvx7jx4/Hn3/+iTZt2uDp06dKfZ4+fRpTpkxB3759sWHDBjRt2hRr167FmjVrFNodO3YMfn5+SE5Oxtdff40FCxbg5s2bGDdunNrxu7u7IzExEb///nuZHvfevXvRsWNHuLm5oUePHqhRowb27NlTpj5evnwJALCzsyvT/QwSI4QQolFBQUEMQKk/nTt3lt8nOTmZmZmZMVdXV5aSkiI/npmZyZo0acKMjIzYmzdv5Mc7d+7MALDVq1crnHvNmjUMADt79qz82Oeff84AsD179ii0/eGHHxgAVrt2bbUeFwDm5OTEjI2NmaenJ3v9+rXKdrt27WIA2OzZsxWOX716lQFg3bt3lx97/fo1A8BMTU0V+pPJZKxhw4asevXq8mO5ubmsZs2azNbWlsXFxcmPSyQS5u3tzQCwJUuWlPo4/v33XyYUChkAVqdOHTZ+/Hi2detW9uTJk2Lvc+vWLQaA7d27V35s/vz5TCQSsfj4eIW2hY9p3LhxLD4+nsXHx7OnT5+yZcuWMQCsVq1aLCcnp9Q4DR0lIIQQomGFCcjEiRPZ+fPnVf6IxWKFBOTXX39lANi6deuU+jt48CADwDZt2iQ/1rlzZ8bn81l2drZC2+DgYKW29erVYw4ODiwvL0+hrUQiYZaWlmVKQExMTBgA1r59e5aenq6yXZ8+fRiPx2MJCQlKt/n6+jI+n89SU1MZY/99WI8ePVqp7SeffMIAyM9z48YNBoB99tlnSm2PHj2qdgLCGGMPHjxgY8aMYfb29gpJYceOHVlYWJhS+48//piZmpoqPObHjx8zAGz9+vUKbQsfU3FJ59OnT9WK0dDRJFRCCNEST09PdOvWTeVtAoFA4d+FSzobNmyo1LZx48YKbQo5OTlBLBYrHCsc2k9MTJQfe/36NVq0aKF0TpFIBHd3dyQnJ6vzcAAAnTp1Qps2bfDtt9+iZ8+eOHv2LCwsLJQeS7Vq1VReZmjcuDEuXryI8PBwhTkY7u7uSm2LPhZzc3O8fv0aAFC3bl2ltvXq1VP7MRTGcfDgQQDAmzdvcPXqVezYsQP//PMPBg4ciJCQEIhEIgAFl9KOHDkCHx8fhQmqIpEIdevWxZ49ezBnzhylc/Tr1w+zZs1Cfn4+wsLCsGbNGsTExMDMzKxMsRoqmgNCNI7H48Hf35/rMIpV3kl3mubv7w8ej8d1GERPMMbKfNv7CYW6/ZWnXVHffPMNFi9ejH///Rc9e/ZEWlqa2n1W5LGUp1911K5dGx9++CEuX76M9u3b49GjR7h165b89mPHjiEtLQ2nT59GnTp1FH6eP3+Ox48f4+bNm0r91qxZE926dUPPnj0xffp0BAUF4d27dxg1alSF4jUUlIBUMXFxcViwYAEaNWoECwsLWFlZoU6dOhg5cmSZJ2SVxcaNG7F//36t9U9IZefh4QEAePz4sdJthccK25SVm5sbXr58qTAxFQCkUql8VKGsli1bhqVLl+L69evypbiFPDw8EBsbqzAKU+jx48fg8/nl+hJQOEry7NkzpdueP39e5v7ex+Px4O3tDQAKk3P37NmD6tWr47ffflP6OXr0KIRCIfbu3Vtq/25ubpg3bx6uXbuGX375pcLxVnaUgFQh7969Q5MmTbBlyxZ88MEHWLVqFVasWIF+/frhzp07av0BqSM7Oxu7du1SOEYJCCEl6969O8zMzLB582aFEYWcnBysW7cORkZG6N+/f7n6HjBgAOLj43HgwAGF49u3b1cavSiLJUuW4JtvvsHNmzcVkhA/Pz8wxrBy5UqF9tevX8fFixfRrVs3WFpalvl8LVu2hJOTEw4cOID4+Hj5calUig0bNqjdz/nz51Uu783Ozsa5c+cAAA0aNABQsFz2ypUrGDx4MIYOHar0M2LECHTt2hVHjx5Vq8LpnDlzYGVlhaVLlyolhFUNzQGpQr7//nvExsbijz/+UHoj27BhAyIiIjRynvevSesDmUwGiUQCExMTrkMhRCVra2usW7cOU6dORevWrTF+/HgIhUL89NNPuHfvHpYvXw4XF5dy9b1gwQIcOXIEU6ZMQXBwMJo0aYKQkBD8/vvv8PT0VPlhrK6vv/4afD4fixYtQrdu3XD+/Hn4+/vj0KFDWLduHcLDw+Hr64s3b95gy5YtsLS0xMaNG8t1LiMjI6xfvx6jRo1CmzZtMHnyZJiYmODo0aOQyWQAoNZlzTlz5iAhIQEDBgxAkyZNYGpqinfv3uHnn39GaGgoxo4dK593U/jFbOjQocX2N2TIEJw9exa//fZbqcuBra2t8cknn2D58uX4+eefMWbMGHUfvuHhaPIr4UDPnj0VZpQXZ+LEiczY2Fhhdv3NmzcZAGZubs6kUqn8+OXLl5WW9+H/y88YK3k2OGOMjRs3rsRlikWX5aWkpLAFCxYwDw8PJhKJmL29PRs5cqTSjPV9+/YxAOz8+fPsm2++Ye7u7szIyIjt27ePMVaweuD9Wf83b95k48aNY3Xq1GEmJibM3NycffDBB+z3339Xen4KY05KSmKTJk1iDg4OzNjYmH3wwQfsxo0bSu2Tk5PZ1KlTmYODAzMxMWHe3t7s/Pnz8n6I4SlcBbNy5cpi25iZmSmsgikUGBjI2rdvz0xNTZmJiQlr3bo1O3z4sFI7Va9jxv77m3t/NUh4eDgbNmwYs7S0ZGZmZqxbt27s3r17rEWLFqx+/fpqPS4ArGfPnipvW7FiBQPAWrZsyZKSklhmZib78ssvmYeHBxMKhczOzo4NHz6cPXv2TK14GWNsyZIlSu8DjDH2+++/s2bNmjGRSMSqV6/OZs+eLV8h8/6yZFX++usvNn36dNakSRNmZ2fHBAIBs7W1ZT4+PmzPnj0sPz+fMcZYXl4eq1GjhsoVREXFx8czgUDAOnbsqPCYPv74Y5XtExISmLm5OfP09CyxX0NH735VyLRp0+RLxmQyWbHtDh8+zACwv//+W35s5cqVjM/nMwDs6tWr8uOq3iCKJiAZGRns0KFDzN7entWrV48dOnRI/sNYwXr8oscOHTrEdu7cySwsLJipqal8GV9KSgpr0KABMzc3ZzNnzmQ7duxgS5cuZdWqVWP29vYsPDxcfv7CBKRp06asYcOGbOXKlWzLli3s33//ZYypfuNeuHAha9euHVu8eDHbuXMnW7lyJatXrx4DoPTmX5g4tGnThvXr149t3ryZLV26lFlaWjJbW1uWlpYmbyuVSlmbNm0YADZy5Ei2ZcsWNnPmTGZqasoaN25MCQjhVG5uLrO2ti42qahMfvvtNwaAHTlyhOtQiJro3a8KCQsLY5aWlgwAc3Z2ZqNHj2YbNmxgwcHBCu2io6MZAPbFF1/Ij3Xv3p317t2b2dnZsWXLlsmPd+zYkbm7uyvcv2gCUqh27doqv+29Lz8/nw0aNIjx+XwWEBAgP/7pp58ysVjM7t27p9A+PDycWVhYKJyvMAGpW7cuy8rKUjqHqgQkIyNDqV1mZibz8vJS+nZYmIBMmzZN4XhhHYft27fLj+3YsYMBYJ9//rlC28I3S0pAiK6o+lsoLESmqvaIvpJIJCrrmbRt25YJhUIWExPDUWSkrGgOSBXi7u6O+/fv4/vvv0dgYCB+/vln/PzzzwAK1sTv27cPLVu2RPXq1VG/fn1cuHABQMEEr2vXruG7776DmZkZLly4gMWLFyMrKws3b97E2LFjNRbjnDlzEBAQgB9++AEDBw4EULC87ueff0b79u1Rs2ZNhTLWZmZmaNu2rXziWFHTpk1Te85H0XX5WVlZyM7OBmMMvr6+8ol670+ae3/dv6+vLwDFPR4CAwPB4/GwYMEChbZDhw6Fl5eXyj07DJYsH8iIA9KjgYxYICcVyM8FZLlAft7//6vq33mKx8EAsTVgaguY2qn4sQX4xS/prKr69OmD2rVro0WLFuDxePKVGF5eXpgyZQrX4ant1atX6N27N0aPHg1XV1fExsbi6NGjePz4Mb788ktUq1aN6xCJmigBqWJcXV2xZcsWbNmyBdHR0bh+/ToOHDiAP/74A/369cPjx49ha2sr/+BNTU3F/fv3kZWVJd/7Yfbs2cjKysLVq1chlUo1tgPmxo0b8eOPP2LmzJmYOXOm/Hh8fDwSExNx4cIFODg4qLyvqt0o69Spo/a54+LisGjRIgQGBiIuLk7p9pSUFKUE5P3CSaoKQIWFhaFatWqwtbVV6rN+/foGlYCkS9MRkR6ByIxIRKRHICMrHp+8DClIONJjgMx4gOli1j8PEFspJiVmdoCpPWDnATg2BBzrAyJTHcSiP/r164dDhw4hICAAWVlZqFGjBqZPn46lS5fC3Nyc6/DU5uDggLZt2+Knn35CXFwceDweGjRogJ07d2Ly5Mlch0fKgBKQKqxGjRoYPHgwBg8ejNGjR+PIkSM4ffo0PvroI/j6+mLLli24fPky7ty5A3t7ezRp0gRisRhSqRRXr17FxYsXAQBdunSpcCwBAQGYO3cuBgwYoLScjv2/YE+XLl3w5Zdfqt2nqal6HzAymQzdu3fHs2fPMHPmTLRu3RpWVlYQCATYt28ffv75Z/kM+6KKK5zE3iswZGjFxmRMhlcpr/Ak6QkeJzzGk8QneJ32GqmSVIV2Qr4QM8JegQddF1xiQE5KwU9SmOomPD5g4wo4NgCqNSz4cWwI2LoDxWytXtnNnTsXc+fO5TqMCrOzs1O5OzCpfCgBIQCAdu3a4ciRI/LiO126dAGfz8eFCxdw584d+Pr6gsfjoW7duqhZsyYuXLiAixcvomHDhmoNeZb0IXzr1i2MHj0aLVq0wJEjR5RGMxwcHGBtbY3U1NRiy1pXxMOHD/HgwQMsXrwYy5YtU7ht9+7dFerbw8MDZ86cQVJSktIoiKodQfWNjMkQnhYuTzSeJD7B06SnyM7LLvW+ubJcxFtWg2NaTKltdY7JgKRXBT/P/vzvuNAUcKhbkIxUa/BfYmKueuSNEFJ+lIBUIUFBQWjbtq3SvAiZTIaTJ08C+K/4jo2NDZo2bYrTp0/j7du32LRpk7y9r68vAgMD8fLlS8yYMUOtc5ubm6vcb+LVq1fo378/HB0dcfLkSZWjFnw+Hx9++CG2bNmCo0ePYuTIkUpt4uLi4OjoqFYs7yscyXh/5OLRo0c4ceJEufosNGjQIJw+fRpr1qzBqlWr5MePHTuml5dfsnKzcDP6Jm7H3saTxCd4lvQMmbmZ5e4vWl8TkOLkZgFRdwt+irJxBdw6A+6dC/5rZs9JeIQYEkpAqpB169bh2rVr6NevH1q2bAkrKyvExMTg+PHjCAkJQZcuXdC3b195e19fX6xbtw4AFOZ5+Pr64tChQ/L/V4e3tzf27t2LpUuXom7duuDxeBg5ciRGjRqFuLg4fPHFF/j777+V7ufn5wczMzMsX74c165dw+jRo3HixAm0a9cOIpEIb968wenTp9GyZctyV1qtX78+GjZsiDVr1iArKwt169ZFaGgoduzYgUaNGuHOnTvl6hco2O9l9+7dWL16Nd68eYNOnTrh2bNn2L17Nxo3boyHDx+Wu29NeZX6Cv9E/IOrkVcREhuCXFmuxvqONrVGU431xqHk8IKfOwcA8IBqjf5LRlw7VLn5JIRoAiUgVciiRYvw22+/4cqVKzh37hySkpJgZmaG+vXrY926dZgxY4bC5Y+uXbti3bp1cHFxUdiDojAZEQgE6Ny5s1rn/u6775CQkICNGzfKyzWPHDkSsbGxAKBUsrnQ69evYWZmBisrK1y7dg3r1q3Dr7/+ij/++ANGRkaoVasWOnTogEmTJpXrOSl8HKdOncK8efNw4MABZGZmolGjRjhw4ADu379foQREKBTir7/+wsKFC3H8+HEEBASgadOmCAwMxE8//cRJApKTl4NbMbfkSUdEhmYq4KoSIzbEyrMMiH1Y8HN9M2AkBlw7Al49Aa9egLUz1wESUinw2PvjzoQQgxORHoHLEZfxT+Q/CI4JhiRfopPzjrZujC/untLJufSGY8OCZKRub6BWa8DAJiEToik0AkKIgcrMzcTZ12cR8DIA9+LvcRJDNKrgZltxjwt+rq4vmDvS7COg+YeApRPXkRGiV2gEhBADwhjD7ZjbCHgZgL/f/q3WahVtqm9RG78++IfTGPQCTwB4dgVajC24TCMQch0RIZyjBIQQAxCVEYXAl4EIDAtEZEYk1+HIWYss8c/zR1yHoV/MHIGmIwuSEXv1i+URYmgoASGkksrJy8Hfb/9GwIsA3Iq5Babzgl/quRWZABNpFtdh6CeXdkDzMUBDP1pJQ6ocSkAIqWRiMmNw8MlBBLwIQHpuOtfhlCowQwj3+GIqkpICxpZAo8EFoyI1W3IdDSE6QQkIIZVEaHIo9j3ah7OvzyKP5XEdjtp28JzwwasbXIdReTg1B3y+KFhJQ4gBo1UwhOi52zG3sefhHlyLusZ1KOUSZVJ5NjrTC1F3gZ+HF4yE+HwJ1NH89gOE6ANKQAjRU/9G/Ysd93fgTlz5C6Hpg2ihMdchVE6RIcDhIQW1RHy+KFhFQ4gBoQSEED3zT8Q/2PFgB+7H3+c6FI2I4dNV3gqJuA38NBhwbgt0+QJw9+E6IkI0ghIQQvREcEww1oesx8ME7veH0aRomW6qrhq8dzeAgwOB2u2BLl8W7EFDSCVGk1AJ4Vh0RjTWhazDX+F/cR2KVtQyrY4zj29xHYbhce1YkIjU/oDrSAgpF0pACOFITl4O9j3ah72P9iInP4frcLRGyBciJOwVeHpap6TSc/cBui0DnJpxHQkhZUIJCCEcOBt+FuuD1yM6M5rrUHTiYqIEDmmxXIdhuHgCoO20ghERkRnX0RCiFkpACNGhZ0nPsOrWKoTEhnAdik4dzrVGk4gHXIdh+KxdgL4baOkuqRRoEiohOpCck4xNdzfh+IvjkDEZ1+HoXLSZNZpwHURVkPK2YOlu42FAr1WAmT3XERFSLD7XARBi6H59/iv6nuiL30J/q5LJBwDEGNM+Jzr18Ddgcyvg7k9cR0JIsWgEhBAtic+Kx+J/F+Nq5FWuQ+FclEDAdQhVT3YyEDgDePAL0G8jYOfBdUSEKKAREEK04Pyb8xj8x2BKPv4vGvlch1B1vb4CbPsA+GcdkF959hAiho8moRKiQRnSDKy4uQInX53kOhS9Ut+iNn598A/XYRDHhsCATUAt2nGXcI9GQAjRkNsxtzHkjyGUfKgQLUnmOgQCAHGPgT3dgHOLaDSEcI5GQAipIGm+FD/e+RGHnh6qspNM1XErMgEm0iyuwyCFarUBhu0DrGpxHQmpoigBIaQCnic9xxdXv8CL5Bdch6L3AjOEcI8P4zoMUpSJLeC3A/DqwXUkpAqiSzCElNORZ0cw+tRoSj7UFGNONSn0TnYS8PNw4PwSuiRDdI6W4RJSRrmyXKy4uQLHQo9xHUqlEm1iwXUIRCUGXNsIFhGMhCHH4GBpwnVApIqgERBCyiA5JxlTzk2h5KMcokXGXIdAShAka4o+m64hODyJ61BIFUEJCCFqepH8AqNOjUJwbDDXoVRK0fRuo7dianbHhBftEZ8uwahdN3DoejjXIZEqgN4SCFFD0NsgjDkzBpEZkVyHUmlFy3K4DoGoILX2wKCID+X/zs1n+DrwMb74/QHy8mlVF9EeSkAIKcXuh7sx+9JsZOZmch1KpRadm851COQ9TGiGablzECMRKd125NY7TDoYjEwJTU4l2kEJCCHFkORL8PmVz/HDnR+ovocGxOYkgoHHdRikiH32n+FCom2xt196Ho/hO64jLo1Gr4jmUR0QQlSIy4rDrIuz8CjxEdehGJSLiRI4pMVyHQYB8ND5Q/R/0VettjWtTbB/fGvUqUYrmYjm0AgIIe8JTw3Hh6c/pORDC6Itq3EdAgGQ5tgaQ8N6q90+MiUbQ7b9ixuvErUYFalqKAEhpIjnSc8x7uw4xGTGcB2KQYo2s+Y6hCov36wahid9DImsbG//aTl5GLvnFgLv0URsohmUgBDyf/fj72PCXxOQlEN1ELQlxtiU6xCqNMYXYrFoPp5llO/3IM2XYc4v9/DL7bcajoxURZSAEALgVvQtTDk3BWnSNK5DMWjRAgHXIVRpZ52m43C0U4X6kDFg4e8PcfjmGw1FRaoqKsVOqryMf/7BrTs7kWVNO7VqWzQvn+sQqqyIWn0x7aW3RvpiDFgU8Aj5Moax7Vw10iepemgEhFRpGf/8g4gZn6Dr7vsYk9KA63AMXnQeJXlckNjWxcC3wzXaJ2PA4sDH2HP1tUb7JVUHJSCkysq4cgURMz4Bk0qB3FwM2PMMI1PrcR2WQYuRJHMdQpXDjC0wMWc2EqVCrfT/7Z9PsPNKmFb6JoaNEhBSJWVcuYKITz4tSD7+j0mlGLL3JYak1+UwMsOWLE1FtogmouoKAw/brOfhapKVVs+z4vQzbL30UqvnIIaHEhBS5WQFByPi05kKyUchlpODkXteYUBGHQ4iqxqirWpwHUKVcdd5LNa80c1rec3Z5zQxlZQJJSCkSpG8fIl3Mz4Bk0iKbcOyszFm71v0yvTQYWRVR4y5PdchVAkp1T/AiLAeOj3n1wGPcOZhtE7PSSovSkBIlZEbG4e3U6ZAlppaaluWmYmJ+6Lgm+Wq/cCqmGgTKuetbXkWNTEkbiJyZbrde0fGgFm/3MP1MKqYSkpHCQipEvIzMvBuyhTkRan/7Yylp2Pa/jh0zHHRYmRVT7TImOsQDBoTiLBQMA9hWSacnF+aJ8OUg8F4HFV6ok+qNkpAiMFjUikiPvkUkufPy37f1DTM3J+Edjm1tBBZ1RTNp/0vtSmw+kwci+F2z510SR78993G20Radk2KRwkIMWiMMUR9+RWybtwofx/JKZh7KA2tJBWrIEkKxMiUJ/8SzXhdaxBmh7XgOgwAQHy6BGP33kRCRvHzrUjVRgkIMWhxa9ci7c8/K9yPLCEJn/+UhSZS2s21oqJzqdy9NmTbNcKA8MFch6EgPDELHx8KgTRPxnUoRA9RAkIMVtKhn5C0Z6/G+mNxCfj65zw0kDporM+qKDYnCQy6nRxp6GRia4zL/ATpefq3u0bIm2QsDnzEdRhED1ECQgxSxtVriF25UuP9suhYLDsK1Mmz03jfVYVUJkWihSPXYRgMBh42WM7HrRRLrkMp1tHb73DwejjXYRA9QwkIMTi5MTGImj8fkGln2JdFRmPFL0Zwz7PRSv9VQZQVXcrSlJvOk7DprRvXYZTqm5NPcOMVLc8l/6EEhBgUlpuLyDmfIT9Zu3uOsLeRWH3MBC751lo9j6GKNrPmOgSDkFijMz582ZnrMNSSJ2OYcfgOIlOyuQ6F6AlKQIhBiVu7Ftl37+rkXOz1W6z93Rw18qmwVlnFGNN+MBWVZ+mCgdH+yGeV5208MVOKKQeDkS3N5zoUogcqzyuXkFKk/XUOSQcO6vakL8OxMdAGjvnmuj1vJRctEHAdQqXGjMSYg7mIyKl8Rd0eR6Vh4e8PuA6D6AFKQIhBkIaHI/qrrzg5N+/5K/z4pz3sZPStXl3RPPoGXBG/OM7BybjKuxor8F4UjoVEcB0G4RglIKTSk+XkIGLWbMgyMjiLgf/kJTafrgErmZizGCqT6DyqkFleL5yHYuGrxlyHUWFLAh/hTWIm12EQDuldAuLv7w8eT/c1AlxdXeHj46Pxfvfv3w8ej4dLly5pvO+y0tZj5FrMsm/KVWZd0wQPn2PbOWdYyCrfsLiuxUi0O0nYUGU6NMOgV4O4DkMjMqX5mHnkLnLzqUhZVVWmBKTww3T//v0qbw8PDwePx4O/v78GQvvPpUuXsHTpUqSkpGi03/IofIxTp05Vuu358+dwcXGBmZkZzp49y0F0VU/K7yeQeuIE12HIGd19iu0XXGEqE3Idil5LlqYiR8jNZmmVlczEDh+mTkdmvt59byy3+xGpWHculOswCEf07pW8a9cuZGcrLtO6dOkSli1bphcJSHHu3r2Ljh07Ij09HefOnUOvXr0AAGPGjEF2djY6derEcYSGJzcmBrErVnAdhhJh8GPsuOwJY0YTLUsSbU1766iL8QRYZTYf99IMb7Lzzith+PdlAtdhEA7oXQIiFAohFleu6+hXrlyBj48PBAIBLl++jPbt28tvEwgEEIvF4PP17qmu9KKXLOF03kdJjG88xM5/6kFESUixos3tuQ6h0rhSawp2RrhwHYZWyBgw59d7SM6kTQqrGq1/KhZesli6dCkCAgLQsmVLiMVi1KhRA/Pnz0deXp5C+/fngPj4+GDZsmUAADc3N/B4PKXLQNHR0Zg2bRpcXFwgEong5OSEKVOmIC4uTimep0+fom/fvjA3N4e1tTUGDhyIV69elfvxnTp1Cr169YKdnR2uXr2KJk2aKNyuag5I4bGLFy9i9erVcHd3h7GxMby8vHDgwAGlc8hkMqxatUqh3aZNm4qdX1LWx7hv3z60atUKpqamsLCwQJcuXXDu3DmldoVzSO7du4du3brB3Nwcjo6OmDt3LvLy8pCTk4N58+ahZs2aEIvF6NixIx4/fly2J1RNKQEByLx8RSt9a4rJtfvY8W8DGFWiOg26FG1C9VPUEevUDf4vO3AdhlbFpkmwKID2i6lqdLZz0enTp7F161ZMnToVkyZNQmBgINauXQsbGxt8+eWXxd7vq6++gq2tLU6cOIENGzbA3r7gW9MHH3wAAHj79i3atWsHqVSKiRMnwsPDA2FhYdi6dSuCgoIQHBwMKysrAMDr16/RoUMHZGVlYfr06XB3d8eFCxfQpUsXZGWVfVb+zz//jHHjxqFu3bo4d+4cnJzKNqT8xRdfICcnB1OnToVIJML27dvh7+8PT09PhVGUWbNmYfPmzejQoQNmzZqF1NRUrF69GtWrV1fqs6yP8csvv8TKlSvRsmVLfPvtt8jJycGePXvQq1cvHDp0CB9++KFC+4iICPTo0QOjRo3C0KFDcf78eaxfvx4CgQBPnz5FdnY2Fi5ciISEBKxduxaDBg3Cs2fPINBg3Ye8+HjErlylsf60yezKXWw3aoGPvR8iH4zrcPRKtIgm65Ym18odAyM/AmOGv3nfqYfRGPQkFt0bUJn+qkJnCcjjx4/x+PFjuLq6AgCmTp2Kxo0bY9OmTSUmIN27d8e1a9dw4sQJDBo0SH7/Qp988gmkUinu3r2LWrVqyY8PHToUbdu2xYYNG7B06VIABclMUlISzpw5I5+jMWPGDHzyySfYsmVLmR7PX3/9hV27dqF169Y4ffo0bG1ty3R/AJBKpbh9+zZEIhEAYNiwYXB3d8fmzZvlCcjTp0+xefNmdOnSBefPn5d/kE+cOBH16tVT6rMsjzE0NBSrVq2Ct7c3Ll++DGPjgg+EqVOnolGjRvj0008xaNAgmJmZye8TFhaG48ePY/DgwfK2rVq1wtq1azFw4ECcP39ePoJlZ2eHWbNm4fz58/JYNCHmm28gS03VWH/aZnnxDrYJWuLjlvdRBT5H1BbNp4SsJExoihn5nyFGIuI6FJ1ZHPgI7TzsYG6sf7v6Es3T2djw+8kDj8dDly5dEBMTg4xyXsdPSUnBqVOn0K9fP4jFYiQkJMh/XF1d4enpKb+UIJPJcPLkSTRt2lTpw7CkBKg4sbGxkMlkqFWrFiwty7cL5fTp0+XJBwDUrFkTXl5eePHihfzYH3/8AQCYM2eOwihCzZo18dFHHyn0V9bHGBgYCMYYFixYIE8+gILEYfr06UhOTkZQUJDCfWrVqiVPPgq1b98ejDF88sknCpfPOnbsCAB4+fJlyU9EGaSdOYP0839rrD9dsT4fgi33m3Edhl6JkdE1/5Icsv8M5xLK/sWmMotOzcGas8+4DoPoiFYSEFV1PNzd3ZWO2dkVbGmemFi+HRJDQ0Mhk8mwf/9+ODg4KP08f/4csbGxAIC4uDhkZGSgfv36Sv04OTnJL9Oo66OPPsK4ceNw/PhxjBgxArm5uWWOv7jnpOjz8fr1awBA3bp1ldq+PwJS1sdYOC+kYcOGSu0bN26s0KbQ+yNQAGBjY6PytsLj5f39vi8vORkx3y3XSF9csD8TjE0Pm3Mdht6Izk3jOgS99cR5FBa/bsB1GJz46cYbhLxJ4joMAAVzEFW95xHNKNM4l4lJwbr995fJFiqcY1DYrqiS5gAwVr6h2ML7jRo1ChMmTFDZ5v1YNFXkjM/nY+/eveDz+di3bx+GDx+OX3/9FUKh+vUfintOij4fJT03xd2m7mMsT98l/R7VeTwVEfvdcuRrKJnhSrU/b2O9oA0+a3CH61A4F5uTBAYeeDQ3RkG6YysMedWH6zA4I2PAwuMPcWpmR4iMtDNIHxcXh7Vr1+L06dN48+YN+Hw+HB0d0bJlSwwfPlxplJdoR5kSEDc3NwDAkydPVN5eeLywnaYU94Hq6ekJHo8HiUSCbt26ldiHo6MjzM3NVcYeFRWF1HLMKeDz+di9ezd4PB727t2LoUOH4rffflO4rFJRhaMkz549g5eXl8Jtz9+r/lnWx+jh4QGgYH7O+yMshatXCttwLT0oCGmnTnEdhkbUCryFNUZtsMCraichUpkUiRYOsE9XXq1WVeWbOWJ40lRk51ft5dsv4jKw7VIYZnWro/G+3717h9atWyM9PR0ffvghpk2bBqDgUvGpU6eQkZEhT0DOnTunsS9QRFmZ0ssWLVrA2dkZR48eRVRUlMJtUqkUmzdvBo/Hw4ABAzQapLl5QfGd5GTF8s12dnbo06cPAgMDce3aNaX7McYQHx8PoCBZGDBgAO7fv69UpXRFBYpZFSYhkyZNwh9//IEhQ4ZAKtXcte3+/fsDADZu3Ij8/P828IqMjMRPP/2kFEtZHuOgQYPA4/Gwdu1ahZiTkpKwdetW2NjY6EXpdiaVInbFSq7D0CjX47ewPKwF12FwLtqSVjwUYnwjfGM8H08zaFNDANgS9FIre8V8//33iI2NxdGjR7Fz507MmDEDM2bMwIYNGxAaGort27fL24pEIoX5cVVZZqbmfxdlSkCMjIywbds2pKamonHjxvj888+xc+dOfPvtt2jRogUuX76MhQsXqpyvUBHe3t4ACpatHjhwAEePHpXPjdi2bRtq1qyJLl26YOLEidiyZQs2bdqEOXPmwMPDQ2Hlx3fffQdbW1v4+flh3rx52Lp1K4YMGYKTJ0/Kl/eWB4/Hw86dOzFlyhT8+eefGDx4MCQSScUe9P81aNAA06dPR1BQEHx8fPDDDz/g22+/hbe3t3wOSNERorI8xjp16mDhwoW4fv062rdvj/Xr12PFihVo3bo1YmJi8OOPPyqsgOFK0sGDyH33juswNK7Or7ewNLxqJyHRZjZch6A3zjtNx4GomlyHoTek+TKsPK35CamhoQWl37t06aLy9qKrKVXNASk8FhERgeHDh8PGxgZmZmbo2bOnvO+i3r17hxEjRsDa2hrm5ubw9fXFnTt3VPZ97tw5jBgxAu7u7jAxMYG1tTV69OiBy5cvK/VbeP9Xr15h4MCBsLKygoWFBQYOHKhy4n9WVhYWLVqEOnXqwNjYGA4ODhgxYoRSzEVrd/3yyy9o2bIlTExMMGPGDHmbv//+Gz169IC1tTXEYjGaNGmikLipq8xrnfr27Ytr165hzZo1OHDgABITE2FmZobmzZvjl19+wfDhw8scRGk6dOiA5cuXY8eOHZg4cSLy8/Oxb98+uLm5wdnZGSEhIVi9ejUCAwNx+PBhiMViODs7o3///grxuLm54erVq5g3bx62b98OgUAAHx8fBAUFwdfXt0Ix8ng8bN++HXw+H9u3b4efnx9OaGiPkk2bNqFmzZrYuXMnFixYAFdXV3z11VfIy8tDcHCwwjyXsj7GFStWwNPTE1u2bMFXX30FgUCAVq1aYevWrejZs6dG4q+IvIQEJGwr+wu7smhw5BYWfdQa3znf5ToUTkQb07d9AIiq2QtTXrblOgy9c/ZxDG69TkIbN82tBiq8rL1r1y7Mnj27XPMCMzMz0blzZ7Rr1w4rVqzA69ev8cMPP2DgwIF49OiRfD5ccnIyOnTogMjISEyZMgWNGzfGnTt30LVrV5Vfevfv34+UlBSMHz8eNWrUQGRkJHbv3o2uXbsiKChIvrKwaBxdunRBmzZtsHLlSrx48QJbt27FzZs3cefOHXltqry8PPTu3RtXrlyBn58fZs+ejTdv3mDLli3466+/cP36daXFCwEBAdi0aROmTZuGqVOnyld77ty5E1OnTkXbtm3x1VdfwdzcHOfPn8e0adMQFhaG77//Xu3nkcfoAlelVVjbIzo6WmVRMkMQtWgRUo8d5zoM7eLxEDy2JdY43eM6Ep37yKYJPr/zJ9dhcEpi44UOiV8hXkobGKrStJYVAma019gCglevXqF58+ZIS0uDs7MzOnbsiNatW6Njx45o2bKlQlsfHx+Eh4cjPDxc4djly5exevVqLFiwQH78+++/x4IFC3D27Fn5l7eFCxdi9erV2LNnj8JCiR9//BGzZs1C7dq1FfrOzMxUGnWOjY1Fw4YN0aZNG5w+fVopjlmzZmHjxo3y4ydOnMDgwYMxadIk7Nq1CwCwe/duTJ48GbNnz8aGDRvkba9du4YOHTqge/fu8pIV4eHhcHNzg1AoxMOHDxWuaERHR8PNzQ1+fn44cuSIQpyFBTNDQ0PVnjtINaIrAVWrjiIiInDw4EE0btzYYJOPnOfPkfq7/ux0qzWModXBEMyJacp1JDoXhbzSGxkwJjLHFOksSj5KcD8iFYH3okpvqCZ3d3fcv38f06dPh0wmw88//4w5c+agVatWaNKkCUJCQkrtg8/nY+bMmQrHCkeYi9ZxCgwMhIODA8aNG6fQtuiIQlFFk4+MjAwkJiZCIBDA29sbN2/eVBnLwoULFf7t5+eHunXrIiAgQH7sxIkT4PF4WLRokULb9u3bw9fXFxcuXEBamuKy+L59+ypNpzh27BgkEgnGjx+vUHcrISEB/fv3h0wmw4ULF1TGqQolIJXAgQMH0L59eyxfvhy7du3CwoUL0bRpU2RlZWHNmjVch6c1cWvXATIZ12HoBmNod+AuZsQ1Kb2tAYnOK/sWCIZkh808XE6keTClWXP2GXJy80tvqCZXV1ds2bIFERERiIqKwvHjxzFgwAA8fPgQ/fr1Q1JSyXVInJyclDZNVVXX6vXr1/D09FQqUSASiVTWgQoLC8PIkSNhY2MDCwsL2Nvbw8HBAadPn1ZahAEA1tbWKr+A1q9fHwkJCfKVj69evUK1atXkMRbVuHFjyGQyhZEYoGCO4PuePn0KAOjZs6dS3a3u3bsDgLz2ljqo3m0l0KJFC/n1uKSkJJiamsLb2xtffvklOnfuzHV4WpF54wYy//mH6zB0SyZD5/33kTuhMXbaV42NuWIkym+qVcU957FY9cKr9IYEUak52HP1NWZ08dR43zVq1MDgwYMxePBgjB49GkeOHMHp06eVKk0XpYm6Vu+3S09PR8eOHZGVlYXZs2ejcePGsLCwAJ/Px8qVK3Hx4kWlPoq7LFXYd+Ht5an5ZGqqPD+rsO2+ffsUJusWpSqxKg4lIJVAmzZtlJbVGjLGWMHoR1WUn49u+x5BOqkh9ttoZydhfZIsTUWO0ATiXNXFDQ1VarW2GBbG/STvymTbpTCMaO0Me3PtLYtt164djhw5gsjISI305+bmhpcvXyI/P18haZFKpXj9+rW8WjQAXLx4EdHR0di7dy/Gjx+v0M/7l04KJScnIyYmRmkU5NmzZ7C3t5df5vHw8MCZM2eQmJioNAry+PFj8Pl8tSq+FtaisrOzK7X2ljroEgzRO+lnzyLnUdUYAVApLw999jzBhynKJfUNUbR12XaRruzyzWtgaMJk5MpoZ8KyyJDkYeeVV6U3LEVQUJDKeXWFe2kBBeUPNGHAgAGIj4/HgQMHFI5v375dac5FYYLy/ojEuXPnip3/AQCrVinuDH7ixAk8f/4cgwYNkh/z8/MDYwwrVyrWU7p+/TouXryIbt26qbWn2bBhw2BsbIylS5eq3F09NTW1TCUoaASE6BXGGOI3l21nYoOUm4tBe0ORO6kefrU07M25os3t4BYfxnUYOsEEInwpnI8XCcrbVZDSHbr+Bh93coddBUZB1q1bh2vXrqFfv35o2bIlrKysEBMTg+PHjyMkJARdunRB3759NRLvggULcOTIEUyZMgXBwcHySa6///47PD09kZf33yTsDh06oHr16pg7dy7Cw8NRq1Yt3Lt3D4cOHULjxo3x8OFDpf7t7e3x+++/IyoqCj4+PvJluNWqVcOyZcvk7fz9/XHo0CGsW7cO4eHh8PX1lS/DtbS0VFhFU5JatWph27ZtmDRpEurXr4+xY8eidu3aiI+Px8OHDxEQEIAnT56ovX8OjYAQvZJx8SKkYVXjw6g0TCLBsL0vMTBD8+Wo9UmMiQXXIejMn9U/wS/RhrlqTReyc/Oxo4KjIIsWLcKECRPw7NkzrFy5ElOmTMHq1ashFAqxbt06nDlzBny+Zj4a7ezs8M8//2Dw4ME4fPgw5s2bh/DwcFy8eBGWlpYKNZysra3x119/wdvbG5s2bcLcuXPx5MkTnD59Gi1aqC5YaGZmhkuXLkEikWDhwoXYvXs3evXqhatXr8prgAAFRUTPnDmDL7/8Evfu3cPs2bOxZ88e9O3bFzdv3lS5gWlxxo8fjytXrqBFixbYsWMHpk+fjk2bNiE6OhrffvttmVZlUh0QolfCR45C9r17XIehV3imptg/0QWnzJWrGxqCqVaNMeOeYezzU5I3tQag88uRXIdR6ZkIBbi20Be2Zprbc0vX8vLy4ODgAG9v73LP71NVo6SyoREQojeyQkIo+VCBZWXBf9879MhUf3Z5ZRLNN/zvQDl2DTDgzVCuwzAI2bn52Hv1NddhqE3VfJOtW7ciJSUFPXr04CAi/UFzQIjeSNy1m+sQ9BbLyMTk/THIneCKIJNwrsPRqBiZ5jZv1EfM2Arjsz5Fai693WrKwevh+LizOyzE+l/ArU+fPqhduzZatGgBHo+Ha9eu4ZdffoGXlxemTJnCdXicohEQohckL14gQ8WGS+Q/LC0N0/fHo32OM9ehaFRUblrpjSopBh5+tJqP68lWXIdiUNJy8nDoxhuuw1BLv379cO/ePSxevBhz587F9evXMX36dFy9elW+03tVRXNAiF6I+nwhUgMDuQ6jUuDZ2uB7f0vcMtZMrQKuifgiBIeFgQfDeyu65TwRw1905ToMg+RgYYx/F/pCKKDv0ZUV/eYI53Kjo5FaZJMlUjKWlIz5hzLRXFqD61A0QiqTItHCgeswNC6pRkeMeql6y3dScfHpEpx5FMN1GKQCKAEhnEvavx/IzeU6jEqFxSfgy58kaCx15DoUjYi2rMZ1CBqVZ+mMQTHjkc/oLVabDl0P5zoEUgH010E4lZ+aiuTfjnEdRqXEYuOw+IgM9XLtuQ6lwqLNDGdDNmYkxjzeXLzNFpfemFTI7fBkPIky3DlEho4SEMKp1MBAMBUlfYl6WFQMvv2Fjzq5yrtcVibRxsobX1VWxx1nISDWMEamKoNDN8K5DoGUEyUghFMpv5/gOoRKj72LwopjQrjmWXMdSrnFGBW/u2hlElZrMOa9asp1GFVKwN0opGbTJdzKiBIQwpmcp08heWbY+5zoCguPwJrjZqiVVzmXe0Yjn+sQKizLvgkGhPtxHUaVk52bj9+C33EdBikHSkAIZ2j0Q8NevcH6AEtUz698tQWi8yv3ZTiZiS0+Sp+BzDzDGMmpbH668UZpF1mi/ygBIZxgUinS/r/1NdGgF6/xw0k72MvMuI6kTGKkKVyHUG6Mx8f35vNwJ7XqbKqnb8ITs3DrdRLXYZAyogSEcCI96BLyU1K4DsMg8Z6GYfOfjrCRVZ4t35MkKcgRVp54i7pWazK2vXPlOowqL+BeFNchkDKiBIRwIvX337kOwaDxH7/A1rNOsGKVZylotLVT6Y30TLyTL8a87MR1GATAmUfRyM2XcR0GKQNKQIjO5cbFIePqVa7DMHiC+8+x7ZwLzFnl2LY82rxyLSXOtXLDgKixYIzHdSgEQEpWLi49j+c6DFIGlIAQnUv74w8gv/KveqgMjO48wfYL7jCV6f+uoTEmlWcOBROaYqZsDqJzKkdyV1UE3DOM/ZGqCkpAiM6lnAjgOoQqRXT7EXZc9oQx0+8VGtGiynO56Cf7OTgTX/kr0BqaC09jkSHJ4zoMoiZKQIhO5YSGQhoWxnUYVY7xjYfYcbUeRHqchETzK8cyymfOI/D164Zch0FUyMmV4S/aoK7SoASE6FTGpctch1BlmV69j+3XG0AA/ZyzECOTch1CqTIcWsDvVT+uwyAlCLxPq2EqC0pAiE5lXLrEdQhVmvnlu9h+qzF4ejjYEJ2bznUIJZKZ2mNUyjRk5+vvKBIBroclID2HSrNXBpSAEJ3JS05G9v37XIdR5VlduINtd5rpXRISK0kC09PRGcYT4DuT+XiYXrkKvFVFufkMV18kcB0GUQMlIERnMv/5h1a/6Anbc8HY/KA512EokORLkGiunxM7L9aahr2RzlyHQdQU9DyO6xCIGigBITqTHhTEdQikCIfTt/HDI/1KQmKsqnMdgpKYmj0w8cUHXIdByuDS83jaG6YSoASE6ATLy0Pm1Wtch0HeU+Pkbax71oLrMOSizGy4DkGB1NoTAyI+5DoMUkZx6RI8jkrjOgxSCkpAiE5kBYdAlq7fkwyrKucTt7D6hX4kIdHGplyHIMdEZpiaOwdxEv0v4kaUXXxGl2H0HSUgRCdo9Yt+czt2C9+94j4JiTHSnxUme2zn4WKifo3IEPXRPBD9RwkI0QlKQPSf1y+3sOQNt0lINPRjkvID54/wXXhdrsMgFXD/XQqSMvW/tkxVRgkI0TppRCSk4eFch0HU0PDnW/jyHXcTU6Pzszg7d6G0at4YFtaL6zBIBckYcPNVItdhkBJQAkK0LvtOCNchkDJodjgY86OacXLuGGkKJ+ctlG9WHUMTpkAio7dGQxD8JpnrEEgJ6K+MaF1WyB2uQyBlwRhaHwzBrNimOj91kiQFEiNuNqVjfCG+Fs1HaKYJJ+cnmkcJiH6jBIRoXfYdSkAqHcbQ/sA9TI9vovNTR1s76fycAHDG6RP8HF2Dk3MT7XgSlYqcXP2YV0SUUQJCtCo/LQ2Sly+5DoOUR34+fPY/wKTERjo9bbSF7quhvqvVF9Nfttb5eYl25eYz3HuXwnUYpBiUgBCtSniVgPSuY5FX05PrUEh55OWhx95HGJesu+3nY0wsdXYuAMixrYcBb0bo9JxEd4LDk7gOgRTDiOsAiGF7E8HD7bw2QJ02MGthBDsLKayzI2D27j6MH/0Dfk4m1yGS0uTlod/ep5BOqo8jVk+1frookbHWz1GIGVtiUs4sJOfSW6Ghonkg+ov+6ohWxb35rxxyZnoeMtP5eAsXwNwF/PYDYGsngK0gBRZJL2H6/DqE4Y84jJYUh0mlGLwnFLmT6uKY5XOtniuar5s9PBh42Gw1H1ffWunkfIQbd94kgzEGHk8/d1quyigBIVoVF178fgyyfIaEuDwkwBxAM8C1GUwaGsHOKg/W0miYRzyA+NEV8DNSdBUuKQGTSDBibxikE+vgD4sXWjtPjEw3xaPuOPtj3QsPnZyLcCctJw/hiVlwszfjOhTyHkpAiNakJ+UgOz23TPfJzsxDRCYQgRqAuAZ4bXrCxtYItsJ0WKa+gumLmxC+vAMe7XTJCZadgzH73iJ3ogfOmIVp5RzRudrfMyi5ensMf9lV6+ch+uF5TDolIHqIEhCiNUUvv5QXkwFJCXlIggmAhkDNhjD2nAw7axms8+JgEfMI4kdXIEimfR90hWVmYsK+SOROcMPfpq813n+sJAkMPPCgnSQzz6ImhsRNRD6jOfhVRWhsOno1qs51GOQ9lIAQrUmO1k5ZbUl2PqKygSg4AIIuQLMusLIxgp04E5bpb2D26jZET2+CJ6P1/9rC0jPw8b5Y5E6ojcsmbzTatyRfgkRze9hnxGu0XwBgAmN8zp+HV1ncFDsj3HgeSztx6yNKQIjWpMbpaF8PBqQm5SEVxgC8AAcvCGuNgZ0NYCNLgEXcU5g8vgJBfIRu4qkiWFoaPjnAR66/M/4Vv9No3zFW1bWSgJyoPhPHw6ppvF+i30JjKAHRR5SAEK1J0VUCokKuRIaYGCAGtgDaAw3bw8LaCHamObDKfAuzN3cgevIv+FIJZzEaApacgjkH+cgd54TbxlEa6zfazBqaLn/2qpYfPnvJ3UZ7hDvhiZmQ5skgMqLLbvqEEhCiNSlx2VyHoCA9JQ/pKUYA3AFrdxh1Hg5bWx5skATLxOcweXoNRpFUtbWsWGISFhziY/nY6rgnitFIn9HGmp0wmG3fCAPD/TTaJ6k8cvMZXiVkoF513Ra5IyWjBIRohSQrFzkZZVsBo2t5uTLExQJxsALw/2JpLY1gZ07F0sqKxSfgq8OOWPqhIx6LKj4hONpIoIGoCsjENhiT/inS8+jtrip7HpNOCYieob9IohUpsfo1+qGuzLQ8ZKapKJbGT4FFMhVLKwmLicPSI9WxaLQ9ngsTKtRXDE+mmZh4fKy3mI/gdxYa6Y9UXmHx9EVC31ACQrSCy/kfmkTF0sqGRcXgu19qYuFIW4QZlX8Pjqg8zXxYXK81CZtfuGqkL1K5RaVUzi9FhowSEKIVOlsBwwEqllYy9i4Sq35zxrxh1nhjlFKuPmKk5btfUQlOPvjwZecK90MMQ3QqJSD6hhIQohX6NgFVm6hYmjIW/g7f/14bc4bIECkoe0G6JEkKJEZiGOfllOv8uZa1MShqHBij/T9IgeiU8r2WiPZQAkK0wpBHQNRRUrE0q/Q3MA27DdEzAy+WFvYGGwLc8IlfPuL4Zb+kEm3tBNeEV2W+HzMywRw2FxE5uttVl+i/KBoB0TuUgBCtSE+ibxsK3i+W5ugFoXMVKJYW+hqb/vDEjAFAQhmTkGgL+3IlIEccZ+PPV/Zlvh8xbDm5MiRlSmFrJuI6FPJ/lIAQrZBk5nEdgt6rKsXSeE9fYrORF6b1iUYyX/1voTEmZV8y+dx5GL580bjM9yNVQ1RKNiUgeoQSEKJxkuw8yGRVcwJmRRlqsTT+w1BsEdTHxz3fIp2vXkIVLSzbB0WmQzMMfjWwPOGRKiI6NQeNalpxHQb5P0pAiMZJMvW7AFllYkjF0ozuPcV2QQNM7vYaWfzSXyPRAvUnkMpM7DEqdToy86nUNileDM0D0SuUgBCNy6EERKsqc7E0YcgT7BA0wmTfl8jhlXyZLlqm3kgJ4wmwwmw+HkSYayJEYsBSs+m9SZ9QAkI0Tt9LsBuaEoulSaJgHvkQ4keXwc9I5TpUAIDxrUfYYdQEkzs9h5RX/CqgmLwMtfq7VOtj7H7hrKnwiAFLz6G5afqEEhCicTQCwr3/iqU5AWInxWJpKWEwfXmL02JpJv8+wA5BM0xu/wR5xZRdj8lJBAMPPBQfY4xTd4x/0UFbYRIDk0YJiF6hBIRoXA6tgNE7isXSGgE1G3FeLM3sn3vYYdQcU9o+Qr6KJEOSL0GiuT3sM+JV3l9q7Y5BkR9qO0xiQDIk9N6kTygBIRpHIyCVgz4US7MIuottgpb4uNV9qCpaGmNVXWUCwoRmmJH3GWIktKSSqC89h96b9AklIETjKAGppDgqlmb9dwi2GrXCtOb3lG6LNrNGIxX32W//Gc6/ttVoHMTw0RwQ/UIJCNE4SRYlIIZCV8XS7M4GY7OgNT5pclfheLSxmVLbR86jsexF/Qqdj1RNGZSA6BVKQIjGyfKpCJkh01axNMdTt7HRqA1mN7gjPxZtJFA8t2MrDAnrU9GHQKoougSjXygBIYRUiCaLpTkF3sL3gjaYX7cgCYkpskw338wRw5KmQiKjYmOkfHLyVK+4ItygBIRoHg2AVHkVKZZW+/dbWDmsDb7wvIPo/ILKlYxvhKXG8/Es0VSXD4MYGBlHy86JapSAEEK0rqzF0jx+u4VvRrbBxnoFu+H+5TQDh17W5O4BEINAe1TpF0pAiMbRlwyijtKKpbnfuY3pAg+8dHHH1FBvrsMlBoDyD/1CCQjRAvorJ2X3frE0q6a1UdMiExfqCuBV3xXZqgqFEFIGYj69hvQJJSCEEL1hYiaBhe1bpMXeRcrbZHS2mI2o8HOYUfsAjpl8jfNpJlyHSCoxq/dWVRFuUQJCNI8GQEgZCIzyYVs9DtKsR4h7/RjJEQUrFfw6zIMkVYoXLxi6u5nCP/UjdLIYi125fngrodUMpOwENACiV/R6PZu/vz94PN2/YlxdXeHj46Pz8xJSldhUS4F9jVvIzdiFdw+PIDbsIZisILFo32oERJECSEwK/v36VWMAgHv6QXwrGQt/q7cQcfDeQCo3Pug1o0/KnYDs378fPB4P+/fvV3l7eHg4eDwe/P39y3sKlS5duoSlS5ciJSVFo/2WV3p6OlasWIHmzZvD2toa5ubmcHNzw6BBg7B7926FtgEBAVi6dGmFz6lvz8H7aACEFMfUMgeOzk8hEhxG9LO9iHhyFdLsLIU2tV2aolaaOwAgx7igcuXLlwxicUsAgBFLR/eUOVgnWgNvc+3tU0MMD42A6Be9HgHZtWsXsrOzFY5dunQJy5Yt04sP3/T0dLRu3RpLlixB/fr18c0332Dt2rUYNmwY3rx5gx9++EGhfUBAAJYtW1bh8+rTc6AKn/7KSRFGonw4OEfAwuo0kt5uw9sHZ5CWEKuyrdjEEu3s+wO5BSMfOaL/EoxXYQ0V2trm3MDM9OFYaHEV9kK9fisjekJIk1D1il7PAREKhRAKhVyHUaxdu3bh+fPn+PHHH/Hpp58q3R4RodlNuyoLkVivX1ZEJxhsqyeDh2eIDbuLd7Hq7RXTp8XHYFH/lcvOFvz3/2FhDO4erZCTE6xwn8ZpG7BacBSnrL7BiVRbGoEjxbI2ovcmfaLTrw2Fl2WWLl2KgIAAtGzZEmKxGDVq1MD8+fORl6e4UdD7c0B8fHzkIwhubm7g8XhKl4Gio6Mxbdo0uLi4QCQSwcnJCVOmTEFcXJxSPE+fPkXfvn1hbm4Oa2trDBw4EK9evVL78YSGhgIAunTpovL2WrVqyf/f1dUVBw4cAAB53DweD5cuXQIA3Lp1C/7+/vDy8oKpqSksLCzQvn17nDhxQqFPTT4HSUlJ+Oyzz+Dh4QGxWAwbGxs0adIEy5cvV/s5UEVkQn/kVZW5dTYcnR9BgIOIerofkU9vIE/Njeo6tR4NYZTiW1I2pAr/DnvZQOV9xfnRGJIyGd+b7kF9E/qWS1SjVTD6hZNPitOnT2Pr1q2YOnUqJk2ahMDAQKxduxY2Njb48ssvi73fV199BVtbW5w4cQIbNmyAvb09AOCDDz4AALx9+xbt2rWDVCrFxIkT4eHhgbCwMGzduhVBQUEIDg6GlZUVAOD169fo0KEDsrKyMH36dLi7u+PChQvo0qULsrKyio2hKHf3guvU+/btw+rVq2FUQna9ceNGrF+/Hv/88w8OHTokP16/fsGunidOnEBoaChGjRqFWrVqITExEQcOHMDgwYNx+PBhjB49WuPPwbBhw3DlyhV8/PHHaNq0KbKzsxEaGopLly7hq6++Uus5UMXYhP7IqxKhSS5s7CORmXwfCa/DytWHm2tz1EhxwfsziLKZYvLy6hWDh6fyKEihGpmn8RXvHG5YLca+jEbIpI0RSRHWQnpv0ic8xspXt3L//v0YP3489u3bp3KiaXh4ONzc3DBu3Dj5t/PCY6ampnj8+DFcXV0BAIwxNG7cGImJiYiOjpb34e/vjwMHDqBoiEuXLsWyZcvw+vVr+f0LDRgwANevX8fdu3cVRh+Cg4PRtm1bLFq0SD4JdPTo0Thy5AjOnDmDXr16ydt+8skn2LJlCzp37iwfnShOcnIymjZtinfv3sHR0REdO3ZEmzZt0L59e7Rr1w58vuK3OVWPp1BmZibMzBS3Hs/KykLz5s0hEAjw5MkTjT4HqampsLa2xvTp07Fly5YSH2dZ3b/4Dld/faHRPol+4fEYbGskgOU+Qeyr+8jPK/8256amVhhQ91OwFOWdSv92fYHwmLcKx9zceKjlfLDUfjNEdfGbydf4m2qHkP/7qIYd1tZz5joM8n+czNwaNGiQwgcnj8dDly5dEBMTg4yMjHL1mZKSglOnTqFfv34Qi8VISEiQ/7i6usLT0xPnzp0DAMhkMpw8eRJNmzZVSD4AlDgC8z4bGxuEhITg888/h4WFBY4fP47PP/8cHTp0UDifOoomH1lZWUhMTERWVhZ8fX3x9OlTpKWlldpHWZ4DExMTiMVi3LhxA+Hh4WrHqQ5jU7oEY6gs7TPgUPMekLsXkY8PISo0pELJBwD0bj5VZfIBAFnSbKVjr18ziMVtSu3XXPoc41M/wjfmf8DZmCapEhoB0Tda/6tUVcej8NJFUXZ2dgCAxMTEcp0nNDQUMpkM+/fvh4ODg9LP8+fPERtbMPM+Li4OGRkZ8ssfRTk5OckvUajDwcEBq1atwsuXLxEfH4/Tp09j7NixCA8Ph5+fH16+fKlWP3FxcZgyZQqqVasGMzMz2Nvbw8HBAdu3bwcAtVa8lOU5EIlE+OGHH/D48WO4ubmhQYMG+OSTT3D+/Hm1H3txTMxFFe6D6A9js1w4uoTBRHwMcS924t2ji8hOT9VI3z5txsIoqvjbM3NUXw59+aKe2ufwSD+A7yRjMc7qHdUOqeJoDoh+KfdXVROTgmHN95fJFiqcR1HYriiBoPgXQTmvCMnvN2rUKEyYMEFlm/dj0XSRM3t7e/Tu3Ru9e/dGzZo1sXLlShw9ehSLFi0q8X4ymQzdu3fHs2fPMHPmTLRu3RpWVlYQCATYt28ffv75Z8hkpVd+LOtzMGXKFAwYMACnTp3ClStXcOLECWzZsgWDBg3C8ePHlS4hqcvEQn9XLhH18AUy2FaPQ17OY8S9foTUCM3X2/B0a41qSU4ornKMjMeQla06AXn9msGzjjdycm6qdS4jlo4eKbPRStwWh4Sf4VYGfRBVRTZCGp3VJ+X+bbi5uQGAwtyEogqPF7bTlOKSBk9PT/B4PEgkEnTr1q3EPhwdHWFubq4y9qioKKSmVvzbXbt27QAAkZGR8mPFxf7w4UM8ePAAixcvVqoT8n4xs5L6KctzUKh69eqYOHEiJk6cCJlMhsmTJ2Pv3r24fPlysat7SiM2pwSksrJ2TIXQ6AXiwu8g4lH5Loeqw9zcBq2seoClFn/5RmrKwEqYRPoitC6cXdRLQArZ5tzArJzheGD1GXZnd0RiLpV0r0qsaQREr5T7EkyLFi3g7OyMo0ePIipKcQxVKpVi8+bN4PF4GDBgQIWDLMrc3BxAwQTQouzs7NCnTx8EBgbi2rVrSvdjjCE+Ph4AwOfzMWDAANy/fx9nz55VaLdixQq1Y7l+/Xqxl0YCAwMBAA0a/LdssLjYC0eE3h/9efTokdIy3JL6KctzkJWVpbTah8/no1mzZgAKluiWl4kFXYKpTEwsJHB0fg5j4VHEPN+Dd4+vQJKpveQDAHo1/bjE5AMApKYlJwfh4QxisXe5zt8kdT3W5E/HYKtkKs5dhdAcEP1S7hEQIyMjbNu2DX5+fmjcuDEmTZoEDw8PxMbG4pdffsHjx4/xxRdfoG7dupqMF97eBW84X3zxBUaNGgVjY2N4e3vDzc0N27ZtQ4cOHdClSxeMGTMGLVq0gEwmw6tXrxAYGIixY8fKV8F89913OHv2LPz8/DBjxgz5Mtzg4GD50tbSHD58GPv27UOfPn3g7e0NOzs7JCYm4vTp0wgKCkKDBg0ULoV4e3tj8+bNmDFjBnr37g2hUAhfX1/Ur18fDRs2xJo1a5CVlYW6desiNDQUO3bsQKNGjXDnzh2NPwehoaHo3Lkz/Pz80LBhQ9jZ2eHZs2fYtm0bnJyc1B5BUUUoEsDIWIA8CZXJ1lcCYT5sq8VAkvkQ8a+fIfmt7kYCfNv6QxBV+qXWHHE+kF5ym9DndeFSu2yjIIUKaodMQjuzftjDJuBZNi3ZNXR2dAlGr1Tot9G3b19cu3YNa9aswYEDB5CYmAgzMzM0b94cv/zyC4YPH66pOOU6dOiA5cuXY8eOHZg4cSLy8/Oxb98+uLm5wdnZGSEhIVi9ejUCAwNx+PBhiMViODs7o3///grxuLm54erVq5g3bx62b98OgUAAHx8fBAUFwdfXV61Ypk6dCmtrawQFBWH9+vVISEiAsbExPD09sWTJEnz22WcKq1tGjRqFkJAQHD16FL/88gtkMhmCgoLg4+ODU6dOYd68eThw4AAyMzPRqFEjHDhwAPfv31dKQDTxHDg7O2PChAkICgpCYGAgcnJy4OTkhLFjx2LhwoVlmoirioWtGMnRmRXqg2gWA4Nd9RTw8Bxxr+/gXVyOzmPw8mgLh4TqUGfHoBxh6atr3rxh8PJqixzJjXLH5JT5JxbxzuK61RLsz2hItUMMmIuYRmf1SbnrgBBSklNbHyD8QQLXYRAAZlbZMLN8jaTIEGQkxnMWh4WFPfq6fQyWrt6y3ed1UvDPu5BS2zm78ODqegia2AYxQ1QPv4q/xoV0cYX7IvrFQWSEh+0bcR0GKYLGo4hWWDtS8ScuCY3zYeMYiezUB4gPD0X5FrdrEI+HXo0ng0WrXzPk/TLsxXn3lqFe3bbIkVwvb3Ry5tJnmCD9EJ0s/LErdyAiJDRJ1VC4io25DoG8h6rzEK2wcjTlOoSqh8dg55QAu2pXkZW4DW8fHEP8m1CuowIAdGs7Afzosn2Yv1+GvSRPn9UBNDid1DN9P5ZLxmKcVQSEVDvEILia0uUXfUMjIEQrrBxoBERXLGwzITYNQ+LbEEQ+Ti79DjpW36sD7OIcUNZLJFl56s9RiXjHUK9eO0gk/5YxuuIV1A6ZhVYm7XFQMBu3M+n7WmVGIyD6hxIQohWUgGiXyCQX1g4RyEi4h/iw11yHUywrq2poatwZLKPs5dozc1UXOSzOs2eecHO7Dk3MBSnKNvsaZuMa7lvNxZ7sDlQ7pJJyM6UERN9QSk+0wsJWDIERvbw0iceXwc4pFjYOl5ARuw1v759AUqT+Jh/g8dCjwYRyJR8AkFVMGfbiRLxjEBt/UK5zqaNp6jqsyf8EflYpVDukEnKlFTB6hz4hiFbw+DxY2tNKAk2wckiHQ807YJI9iHx8GNGhdyDLr9gGcLrQo+1k8GPKN1rAeAyZWWVLQADgyRMPaPNtTZwfiaEpE7HGdD/qmVAaUpm40giI3qEEhGgNXYYpPxMzCRydX0Bs/CtiQ3fh3aNLyMkopSqXHmlYtzNs4mzKfX+pSfn2hYqMZDDW4ihIIafMk/gqeyRmWD2FmYASEX1nZSSALRUh0zv0GyFaY+VgCnC/ALTSEBjlw7Z6HKRZjxD3+jGSIyrnXANr6xpoLOwAJi3/KI3ENB8oZzX4J4/d4eH5LwDtPn98JsUHKYvQWFQfv5ouwkWqHaK33Exo9EMf0QgI0RpbJ7PSGxHYVEuBfY1byM3YhXcPjyA27CGYGrsf6yMej48e9ceDZVbsElGOuPyPPyqKwdi4fYXOXxYW0qeYmPYhlpn/iVrG9Jaqj5pY0GisPqK/FqI11dwsuQ5Bb5la5sDR+SlEgsOIfrYXEU+uQlrM1vOVSc92H4MXU/E9gHKEuRW6/5PHbtD125tn+j58J/HHWKtIqh2iZ5pZUl0ifUSXYIjW2NYwg9BYgFzalA4AYCTKh021aOSkPUD8m+dIMrBdEJo06AqrGM0knTmCio2gREUxNGjYARLJFY3Eoy4hS0XPlJlobdIeBwSzEUy1Q/RCcwtKQPQR/XUQreHxeXB0teA6DI4x2FZPgl31f5GTvB3vHvyK+PBngIElH3a2tdCA11ZjJTiy+epXQS3O40eu4IGb7ddts69hTsYwLLC8Dltajs4pMwEfdc1ofo4+or8MolXVXCu2q25lZW6dDUfnRxDgIKKe7kfk0xvIk1b8Q1Uf8fkCdPMaB5aluaXBWTL19oEpSXQ0g0jUQQPRlF/T1LX4XvYJBlLtEM40NjcBny6J6SW6BEO0qirNAxGJ82DtEInM5HtIeB3GdTg606vdVCBKs3VJsvPVL8NekkePasPLSwAG7i4DivMjMTxlIjqY9cduNh7Psw1r9EvfNaf5H3qLRkCIVhl6AsLjMdg7xcPW8QoyE7bh7YPjSHxXdZKPZg17wCLaXOP9ZkrLVoa9ODExDEKOR0EKOWWexKLskZhu9QymfPpGrivNLWk1nr6iERCiVWZWxjC3NUZGkmFdfrC0y4Cx+CUS3oQg4nEq1+FwwsHeFfVYKzCm+dGFspZhL8ljPRgFKcRnUrRP+QpNRPXxq9nXuJhO9Sm0rRktwdVbNAJCtM5Q5oEYm+XC0SUMJuJjiHu5E+8eXUR2etVMPgQCI/h6fAiWrfkPdcZjyNTgkuSYGAaRqKPG+tOEgtoho7HM4hRqiuhtWFvshUZwoSJkeote+UTrKvNlGL5ABvuaMbC2u4D06K14ez8QydFvuQ6Lc73aTgPitLMfTa6YQabhQmwPH7qAx9O/AV/PtL1YLvXHR1ZRVDtEC2j+h36jBIRoXQ2PyjcCYu2YCgenYORn70bEo58R8/I+ZPncD+HrgxZN+sA8Sntv7Dmmmp+kGRvLIBTq1yhIISFLRe+UT7HOeB1amlXOCrj6qqON5ucnEc2hBIRonaOrJYzN9O/b5/tMLCRwdH4OY+FRxDzfg3ePr0CSWc4NSQxUNUcPeOU20+o5JCbaGVl5+EA/R0EK2WVfw5yM4ZhveYNqh2hIZ9vyj75eunQJPB4Pq1at0mBEpCh6lROt4/N5cKlvy3UYKgmE+XCoFQlLm7NIebcNbx+cQmpcFNdh6SWBQAQf15FgOdodCcoRaqf/uDiZ3o6CFOKBoVnq9/iefYqBVqlUO6QCahgLqQCZnqMEhOhE7cb2XIcgx8BgWz0ZdtVvQJq2A+8e/oK4V0/AGA1/l6RP22lAvHZGJ4rKFlRsH5iSPLjvDB5PqLX+NUWcF4HhKROw2vQgvEwoDSmPTjZVvQqz/qMEhOiES0NbcD3HztQqB47OTyDk/4Sop/sQ+fRf5OZopuCVoWvdtD9Mo3TzbTKbX/EqqMWJj2cQGun3KEhRNTMD8XX2SEyj2iFl5mOr+QRk69at6NGjB2rWrAmRSIQaNWrgo48+Qnh4uFJbHo8Hf39//P3332jbti1MTU1RrVo1zJw5ExkZipd2o6KiMHfuXDRr1gw2NjYQi8Vo0KABVq9ejfz35p7t378fPB4PFy9exOrVq+Hu7g5jY2N4eXnhwIEDGn/M2qS/F0SJQTExF8HR1RKxr9N0el6hcT5sHCORnfoA8eGhSNLp2Q1D9eqe8JA01lkdjWym3Zox9+/XQoOGQjCmvZEWTeIzKTqkfIWmooY4avYVLlHtkFIJeEAXLSQg69atwwcffIDu3bvD2toajx49wu7du3Hx4kU8fPgQdnZ2Cu3v3LmDY8eOYfLkyRg7diyCgoKwadMmPHjwABcvXgSfXzAG8ODBAwQEBGDw4MFwc3ODVCrFmTNnsHDhQrx69Qo7duxQiuWLL75ATk4Opk6dCpFIhO3bt8Pf3x+enp5o3769xh+7NlACQnTGtbGdbhIQHoNdjUQg/xliwu4hPUZ736gNnVAoRmfnEWAJ2r/0UigrT7sJSEICg9CoE6S5F7R6Hk2zkD7GZOlo+FhOwi5JX0RK6ZJhcVpbmsFaqPmPtwcPHsDMTLGy6oABA9CtWzfs2bMHCxYsULjt4cOHOHHiBAYNGgQAmD59OmbNmoUff/wRR44cwYcffggA6Ny5M16+fAlekWHi2bNnY8yYMdi9ezeWLl2KGjVqKPQtlUpx+/ZtiEQiAMCwYcPg7u6OzZs3V5oEhC7BEJ2p3Ui780AsbDPhUOsB+Pn7Efn4ICKf3UJ+LiUfFdHbexqgw+QDALJyNVOGvST379eqFHNBVKmTthvLcyfgI6soGNFVGZW62mmn9lBh8iGTyZCamoqEhAQ0bdoUVlZWuHnzplL7unXrypOPQgsXLgQAnDhxQn7MxMREnnxIpVIkJSUhISEBPXv2hEwmQ3BwsFLf06dPlycfAFCzZk14eXnhxYsXFX6cukIjIERn7J3NYWolQlaq5pICkUkurB0ikJFwD/FhrzXWLwG8m/vBJFJUekMNy9RgGfbiJCTIYGTUGbm5f2v9XNoglCWjd8qnaG3SEQcFsxCSSZlIUd3ttZOAXLx4Ed988w1u3ryJnPfmjyUnJyu1r1+/vtKxGjVqwNraGmFh/+0ZlZeXh1WrVuHgwYN4+fIlGFOshaOqb3d3d6VjdnZ2ePPmjdqPh2uUgBCd4fF4qN3IDk+vRVesH74MttXjIct9itiwB0jT8E6sBKjpVA9uWfXAoNthfsZjyMzK1Mm57t9zQsNGlWcuiCr22f9gDq7ivtV87M5qh+Q8uizjaiJCPTPN7/9y69Yt9OjRA56enli1ahXc3NzkIxcjR45UWb2XV8zMe8aYwm1z5szB5s2bMWLECHz11VdwdHSEUCjEnTt38Pnnn6vsWyAQFNt3ZUEJCNEp18b25U5ArBzSIRK9QHx4CCIfp2s4MlJIJDJFR6ehYIm6/2DWRhn24iQmMhgZ+SA397xOzqctBbVD1mCtUS0EWn6Dk2lWqDwfQZo3uJqNVvo9cuQI8vPzcebMGbi5ucmPZ2ZmqhyhAIAnT54oHYuOjkZqaqrCCMZPP/2ETp064ejRowptX758qaHo9RPNASE65dLQFiIT9fNeEzMJHJ1fQGz8K2JDd+Hdo0vIyaDkQ5t6t5nGSfIBABJT3X6Dv3/PCTye7i8zaYM4LwIjUidglelPqFOFa4cMraadooeFIw7vjzCsWLGi2KT5+fPnCAgIUDi2evVqAICfn59C3+/3m5mZiQ0bNlQ0bL1GIyBEp4yEAng0d8DTf4sfBREY5cO2ehykWY8Q9/oxkiNoWFlXPmgxFOJI7t4WJGIZoJsrMACAxETZ/0dBzunupFpWK/MEvuadwb9WS3AgvS6yZVVnPKSZhSncTbWzTNnPzw8bNmxAnz59MGXKFIhEIpw/fx4PHjyAvb3qCfaNGzfGRx99hMmTJ6NOnToICgrCsWPH0LlzZ4waNUrebujQodixYwdGjBiBbt26ITY2Fnv37lVa1mtoaASE6JyXd3WVx22qpcC+xi3kZuzCu4dHEBv2EExHw/EEcK7ZEC4ZdTiNIVuk+/k89+5WN5hRkEICloOOKV9gvdFi+FhUnZVgQ6tr7vJL4YhE4chH+/btcfz4cZiZmeHrr7/G0qVLYWJigsuXLystzS3UokULBAQE4N9//8XcuXNx5coVfPLJJzh58qS8BggArF+/HvPmzcONGzfw6aef4sCBA5gyZYrB70PDY5VpxgoxCEzGcPCrf5GRLIGpZQ7MrV4jJeoO0hJiuQ6tyjIWm8Gv4RywJG4nZL7wTMXlCOUlh9rm6xuH3Ly/dH5eXQm1nITdBl47xIgH3P2gIRxEmlleHRgYiEGDBmHnzp2YPHlyme/P4/Ewbtw47N+/XyPxGCIaASE6x+Pz0KC9ESysTiPp7Ta8fXCGkg+O9Wk1jfPkA9BuGfaS3L1bHXy+4VYY9fp/7ZAPraINtnZIJxsLjSUfAHDjxg0ABZdRiHZQAkI44d7MCfHhzwAagONch1YjIYpUvaRP17Jl3CQgycky8Pk+nJxbV4SyZPRJ+QTrjH9AczPD+7sbWl0zk0+PHDmCuXPnYt26dWjSpAm8vb010i9RRgkI4YRdLWfU8KrHdRhVXm2XpqiZ5lZ6Qx3JknG3OeDdO9UMehSkkH32FczNGIZ5lrdhbWQYHwFmAj562VtppK/p06dj3759GDRoEE6dOlVsLQ9ScbQKhnCmkU93RIc+4zqMKktsYol29v3Bkrm/9FIoS6r9MuzFSUlh4PO6QIaznMWgKzwwNE9dhbVGLgi0XIY/0ywrde2Q3vZWMBVoJpkqrqZHWdH0ytIZRvpLKqV6H3SE0Fg3W7wTZX1aTtWr5AMAMiXcJSAAcPduNfD5Vec1aZL3FiNTx2OV2WF4VuLaIWOdDHu5qqGiBIRwRmRiirofdOQ6jCqpU+vREEbq3weOrsqwFyclRQY+rwunMXChVsbvWJw9Gh9bhcKEr3+vi5I0szBFG2tzrsMg5UAJCOFUyz4DuQ6hynF3bYkaKS5ch6FEKmbIz8/nOgzcueMAPl/ze4noOwHLQaeUL7DeaCk6V6LaIVOcHbgOgZQTJSCEU/YurnBt1pLrMKoMU1MrtLHuDeTp3/VpqZl+1KhITWXg83y4DoMzlpIHmJI2CovNz8JJpN8fETWMhejvYM11GKSc9PvVRaqEVv38Sm9ENKJ386lgqfo176NQjjH3ox+F7txxrJKjIEXVTd+FFbkTMNoqRm9rh4yvaQ9hJbtkRP5DCQjhXO3GzeDo6sF1GAbPp81YGEVxHUXxcjgow16c1FQZeFVwLsj7hLJk9E2ZgXXiH/WudogJn48xNPm0UqMEhOiFVv1pFESb6ri3QbUkJ67DKFGOkf4kIABwJ8QefL4p12HoBfusy5ibMQxz9ah2yLDqNrARUiWJykw/XkmkyqvbriMs7GkymTaYm9ugpUV3IF+/vsG+L5unXxMf09IYeKBRkEI8MLRIXYW1bBb6W6ZxHAswuRa9X1R2lIAQvcAXCNCi9wCuwzBIvZp+DJamX6MLqmRDwnUISkLu2EFAoyAK/qsdcgSeYm7mX/jYWqCOWdWp12KoKAEheqNJ154wNlW9rTUpn65t/SGI0u+Rj0JZ+fqXgKSnMTAaBVHJOeMYFueMxsdWLyHW8UTQGS6OOj0f0Q5KQIjeEJmYokm3XlyHYTC8PNvCPqE612GoLSuX2yqoxQkJtoNAQImxKgW1Qz7HeqNl6KSj2iEdbczRwcZCJ+ci2kUJCNErLfsOovLsGmBhYY8Wpl31ft5HUZk5WVyHoFJGBgOT0ShISawk9/Fx2igstjiHGlquHfKFew2t9k90hxIQolfMrG3QkuqCVAyPh16NJ4Ol6/+8j6Iys/UzAQGAkBBbCARU7rs0ddN2YGXuRK3VDullb4kWljQaZSgoASF6p/WAwTC1suY6jEqrW9uJ4EfrR1VRdeUay5CXp78JU0YGg0zmw3UYlYJQloS+KTOw1ngzmmmwdggfwEIa/TAolIAQvSMSm6DdkFFch1Ep1ffqALs4e67DKDOJnhW5UiUkmEZBysIhOwjzMobhM6tgWGmgdsjgajaoZ1a1q9MaGkpAiF5q0q0XbGrod+EsfWNlVQ1NjTsDMv3/MH9fjlh/yrAXJzOTQUZzQcqEB4aWKSuxjs1CP8v0cvcj5PEw363yTKgm6qEEhOglvkCADiPHch1GpcHj8dGjwUSwDP29jFESiUj/ExAACAm2gUBAKzDKyiTvLUal+mOl2RF4iMv+sTOqhi1qmxhrITLCJUpAiN7yatsBNTzrch1GpdCj3STwYyrHh7gq2Ub6uUHe+zIzGWT5NApSXi4Zx7AkZzSmlKF2iAmfh89cafTDEFECQvRapw/Hcx2C3mtYzwfWsTZch1Eh2bzKkYAAQHCwNQQCS67DqLQELBud/187pKNF6b/3ybUcUN1YqIPIiK5RAkL0Wq0GjeDeojXXYegtG+saaGzUHqhci16U6GMZ9uJkZdEoiCZYSe5jatpIfG1xHtWLqR1S01iIWa7VdBwZ0RVKQIje6/ThBAiMaNfL9/F4fHSvPx4ss3LO+ygqOz+H6xDKJDjYCkY0CqIR9dK2Y2XuJIyyioXgvduWetaEmeD9o8RQUAJC9J5dLWe0HjiM6zD0Ts92H4NXied9FJWpp2XYi5OVxZBHoyAaI5Ilol/KdKwVb0HT/+/919nGAv0drTmNi2gXJSCkUmg7eDhsazpzHYbeaNKgK6xiDOcbeKakciUgAI2CaINj9kXMyxyOudb3sNyrJtfhEC2jBIRUCgIjIXpM+RTgcbP9tz6xs62FBjxvoPKV+yhWZlYm1yGUWXYWQ14ejYJoGh/5GGKVAk9T2hPK0FECQiqNmvUaoGm33lyHwSk+X4BudcaBZRnGpRcAyDVmel2GvSS3b1vCyMia6zAMiqmpB1xdp3EdBtEBSkBIpdJxtD/Mbe24DoMzvdpNBeIq54d1caSmlXcJT04OkCv14ToMA8JDvbrfgc+nomNVASUgpFIxNjVF1wlV89tRs0Y9YRFteHuR5IgrbwICAMHBNAqiKU41hsHGpg3XYRAdoQSEVDqerduijvcHXIehUw72rqgna2lQ8z4K5Ygq94hOTg5DrpTmglSUWFwTdep8yXUYRIcoASGVUtcJ02BsZsZ1GDohEIjg6/EhWLbhzPsoKkdYuRMQAAgOtoCRUeWuRsslHk+Ahg3Ww8iI9tmpSigBIZWSmbUNfP0/5joMnejd9mODm/dRVDZPynUIFZaTwyCluSDl5lp7OqytW3EdBtExSkBIpdWgky/qdzTsoe8WTfrALMqU6zC0qjKVYS9J8G1zGgUpByvL5nBz+5TrMAgHKAEhlVq3idNgXa0G12FoRTVHD3jlNuM6DK3LyjeMBEQiAaQSw06INU0gMEfDhhvA41G59aqIEhBSqYlMTNFn5jzwBYa1V4yRkQg+riPBcgxz3kdRWZWsDHtJgoPNYWRky3UYlUbdustgYkIVjqsqSkBIpVfDsy46jBzDdRga1bvtNCDecOd9FFUZy7AXRyJhkEh8uA6jUqhWbQBqVB/EdRiEQ5SA6ICPjw9cXV11fl5/f3/w9KB0+dKlS8Hj8RAeHq61c7TqPxjuLQ2jfkDrpv1hGll1ylBnZle+MuwlCQk2h1BYdYvlqUMsroV6db/hOgzCsSqTgFy6dAk8Hg88Hg/bt29X2YbH46FXr17l6j8gIABLly6tQISkIng8HnpP/wyWDtW4DqVCalSvAw9JI67D0Jk8EUNubi7XYWiURMKQk+3DdRh6i88XoVHDjbTkllSdBKSoZcuWITNTs9+6AgICsGzZMo32ScpGbG6OAZ99AYFQyHUo5SIUitHJeQSYpHJXBi0LiakBVlYDEBxsCqHQnusw9FK9ut/Byqo512EQPVDlEpBWrVohJiYGGzZs4DoUrdF0clWZVHP3RJdxU7gOo1x6e08DEgxrNKA0OSaGOc9FKgWysztzHYbecXGeiBo1hnAdBtETVS4BGTJkCNq0aYPvv/8eCQkJpbYPDg6Gn58f7O3tYWxsjLp162L58uUKu3e6urriwIEDACC/zMPj8XDp0iWFviIiIjB8+HDY2NjAzMwMPXv2RGhoqNI5JRIJVqxYgYYNG0IsFsPa2hr9+/fH3bt3FdoVXlbav38/tmzZggYNGsDY2Bjff/99sY/n2bNnmD59Oho2bAgLCwuYmpqiZcuW2LVrl1Lbwrkbz549w4IFC1CzZk0YGxujadOmOH36tMq4v/jiC9SqVQtisRhNmzbF0aNHVcbx7t07TJw4EbVr14axsTHs7OzQunVrlXGUVdPuvdGi94AK96NL3s39YBIp4joMncsRGe4qn+DbphAKHbgOQ2/Y2fnA03Mh12EQPWJYaxfVtHr1anTp0gXfffcdNm7cWGy706dPw8/PD56enpg7dy5sbW1x/fp1LF68GPfu3cNvv/0GANi4cSPWr1+Pf/75B4cOHZLfv379+vL/z8zMROfOndGuXTusWLECr1+/xg8//ICBAwfi0aNHEAgK1sHn5uaiV69e+PfffzFmzBh88sknSE1Nxe7du9G+fXtcuXIFrVopVgzcuHEjkpKSMHnyZFSrVg3OzsUva7t06RKuXr2KQYMGwcXFBRkZGfjtt98wZcoUJCQk4IsvvlC6z7hx42BsbIz58+dDKpVi48aNGDRoEEJDQxUm144aNQonTpxAr1690LdvX0RGRmLKlCmoU6eOQn95eXno3r07IiMjMW3aNNStWxdpaWl49OgRrly5gsmTJxcbv7p8xk5CanwswoJvVrgvbavpVA9uWfXAUHUuvRTKMTLcEZ/c3IJRECOjY1yHwjlTU080argRPF6V+85LSlAlExAfHx/06tUL27Ztw+zZs1WuUMnJycH48ePh7e2Nixcvwsio4Kn6+OOP0bRpU3z22We4dOkSfHx8MGjQIAQEBOCff/7BRx99pPKcCQkJmD9/PhYsWCA/5uDggAULFuDvv/9Gz549AQCbNm3CpUuXcObMGYUJsdOnT0ejRo0wb948pZGVd+/e4fnz57C3L/2a89ixYzF16lSFY3PmzIGvry9WrVqFefPmQfjeHAoHBwecPHlSvqKmS5cuaNOmDXbs2IGVK1cCAM6dO4cTJ05g5MiROHLkiPy+fn5+aNu2rUJ/T548wfPnz7FmzRrMnz+/1JjLg8fno++n8/HLsoWIffVSK+fQBJHIFB2dhoIlGu4HcUmy+Yb9uINvm6BjJwfk5sZzHQpnjIys0bTJDpp0SpRU2XR09erVyMvLw6JFi1Tefv78ecTFxWHs2LFISUlBQkKC/KdPnz4ACj501cXn8zFz5kyFY76+vgCAFy9eyI8dPnwYderUQatWrRTOKZVK0b17d1y9ehXZ2Yp1E8aOHatW8gEApqb/lfXOyclBYmIikpKS0KNHD6SlpeHZs2dK95k1a5bCct7WrVvDwsJCIe7AwEAAwOeff65w3zZt2qBbt24Kx6ysrAAAFy9eRGxsrFpxl4dQLMagBYthYa+/w+B92kyrsskHAGSj8u8DU5LcXCArq+rOBeHxjNC40Y8wNXXlOhSih6psAtKkSROMHj0aP//8M+7du6d0+9OnTwEAkydPhoODg8JPvXr1AKBMH55OTk4QixVrO9jZFdQKSExMVDjvixcvlM7p4OCAvXv3Ij8/X2nuyvuXOEqSkZGBefPmwcXFBSYmJrC3t4eDgwO++uorAEBycrLSfdzd3ZWO2draKsQdFhYGHo8nf26KatCggcK/a9eujcWLF+PcuXNwcnJCixYtMH/+fNy4cUPtx6Eucxtb+H2+BCIT/dtP5YMWQ2EcWSUHIeWyZTlch6B1wbdNIBRW7uXh5VWnziLY2rbnOgyip6r0u993332H3377DQsXLsTZs2cVbmOsYHngqlWr0LJlS5X3d3JyUvtchXM8VCk8V+H/N2jQAD/88EOx7R0cFL/RFx3VKM2oUaNw6tQpTJkyBZ06dYKtrS2MjIxw+vRpbNiwATKZ8jyE4mIvGndZLVu2DP7+/jh9+jT++ecf7Nu3D2vXrsWnn36KH3/8sdz9quLg4or+cxbixOplkOXrx6RHl1qN4JJRp0rO+ygqK9fwE5C8PCArsxOEot+4DkWnnJ0nwLmWYVUoJppVpROQ2rVrY9q0adi4cSMuXryocJuXlxeAgg/39y8hqKKpiqNeXl6Ijo6Gr68v+HzNDlClpKTg1KlTGDNmjFIxtr///rtCfXt4eIAxhmfPnqFZs2YKtz158kTlfdzc3DBjxgzMmDEDEokEAwcOxKZNmzBnzhy4ublVKJ73uTZtga4TpuH8rs0a7bc8xGJzfFBtEFhS1b30UsiQyrCXJDhYjM6dq0OaG8N1KDrhVGM4vOp8xXUYRM9V2UswhRYtWgRLS0ssXKi4PKxnz55wdHTEmjVrVC7Xzc7ORnp6uvzf5ubmAFRfwiiLMWPGID4+vtiltBWZM1E4kvH+yEV0dDR2795d7n4BYNCgQQAK5tYUdevWLaXkJjU1Van6pbGxMRo2bAgASEpKqlAsxWnSrRfaDBqmlb7LonerqZR8/J+hlWEvTl4ekJHZkeswdKJatf6oV28512GQSqBKj4AABfMwFixYoDQZ1dTUFAcPHsSgQYNQr149TJgwAXXq1EFKSgqePXuG33//HSdOnICPjw8AwNvbG5s3b8aMGTPQu3dvCIVC+Pr6wtHRsUzxzJo1C+fPn8fChQtx6dIldO3aFZaWlnj79i0uXLgAsViMoKCgcj1WCwsL9OjRAz/99BNMTEzQunVrvHnzBjt27ICbm5vCnI6y6t69O/z8/HD06FGkpqaib9++iIiIwJYtW9CsWTOFGiZBQUGYMmUKhgwZAi8vL1hYWODevXvYsWMHmjRpojSCokkdR41Dfq4UIacCtXaOknRoNRKiSNp6HADyhQxSqWFPQi0qJFiMzj7VIZUa7iiIvX03NKi/lpbbErVU+QQEKFiGumXLFkRHRysc79mzJ27fvo1Vq1bh8OHDiI+Ph42NDTw8PPDZZ5+hSZMm8rajRo1CSEgIjh49il9++QUymQxBQUFlTkCEQiFOnTqFrVu34tChQ1iyZAmAgvkmbdq0wbhx4yr0WH/66ScsXLgQJ0+exIEDB1CnTh0sX74cQqEQ48ePr1DfR44cwZIlS3Do0CFcvHgRXl5e2LFjB54/f66QgDRt2hSDBw/G5cuXcfjwYeTn58PZ2Rnz5s3D/PnzS5wvowk+YyeDyRjunPlDq+d5X22XpqiZ5gZU8XkfhXLMGGD4U0Dk8vKAjPSOEBkb5lwQW5v2aNzoR/D59LFC1MNjFZlJSEgldnH/Dtw9c1In5xKbWGJQ/ZlgyXTppVBSjVz8nnyF6zB0SiAAfLr8Dak0uvTGlYiVVUs0b7YfAoH+rTYj+ovGyUiV5ev/MZr17KeTc/VpOZWSj/cYchn24uTnA+lphjUXxMKiIZo13UPJBykzSkBIldZ1wlQ07dFXq+fo3OZDCCM1s0rKkBhyGfaShIQYQyRSfwm/PjMz80KzpvupyikpF0pASJXXdcJUNO3eRyt9u7u2RPXk4vfmqcoMvQx7cfLzgbS0DlyHUWGWFk3QssURiES2XIdCKilKQEiVx+Px0HXiNDTp1qv0xmVgamqFNta9gTyaZqVKNiRch8CZkGBjiEQ1uQ6j3KytvdG8+SEIhdZch0IqMUpACEFBEtJt0gy0HjhUY332bj4VLLVqfstXR5as6iYgMhmQllo5R0Hs7buiWdN9MDIy5zoUUslRAkLI//F4PHQa7Y9uk2aAV8EqtF28x8IoSkOBGais3KpRBbU4ISEiiES1uA6jTKpX90PjRlshEBhzHQoxAJSAEPKept17Y9D8ryE0FpfeWIU67m3gmGgYkwy1KauKlGEvjkwGpKZWno3aateeioYN1lKdD6IxlIAQooJ7i9YYsXQVzGzKNsHOwtwOLS26A/k076M0mTlZXIfAuZBgIUQifZ+kzIdXncXw9JjPdSDEwFACQkgxqrl7YvR3a2FXy0Xt+/RsMhksLU+LURmGPCMGiaTqzgEpxBgPqSkfcB1Gsfh8EzRq9COcnStWgdnQLV26FDweD+Hh4fJj+/fvB4/Hw6VLlziLS99RAkJICSztHTHymzVwbtik1LZd246HIJpGPtQhNaPnqVBIiBDGejgKIhY7o1XL31DNsbdG+ktNTYWpqSl4PB7279+vkT5Lk5KSgqVLl1ISoKcoASGkFGIzcwz5chnqd+xSbBsvz3awT6imw6gqtxwT2g+nEGM8JCfr11wQW5sOaNM6ABYW9TXW588//4ycnBx4eHhgz549Guu3JCkpKVi2bBknCciYMWOQnZ2NTp066fzclQUlIISoQWAkRJ9P5qLTRxPAf2+zPAtLB7Qw9aV5H2UgMabLVEXduWMEY5H6l/q0ycVlMpo126vxGh979uxBp06dMHfuXFy9ehXPnz/XaP+akpmZqZF+BAIBxGIx+BVcUWfI6JkhpAxa9x+MYYuWw8zapuAAj4dejSaBpdMHalnkGNHzVRRjPCQlczsXhM83QaOGP6CO50LweJrdkfrBgwcICQmBv78/Ro0aBWNjY+zdu1ehTXh4OHg8HpYuXap0f1VzLN69e4eJEyeidu3aMDY2hp2dHVq3bo1du3YBKJiD4ebmBgBYtmwZeDweeDwefHx8AACXLl2SXw7asmULGjRoAGNjY3z//fcAgFu3bsHf3/9/7d15XFNXFgfwXxJJAgkGgYAgCIRNdqyAWm1RKlLbGRe01YpVsSNWse1AKy517GhdmVIXRiszUlAL2rFqa5WqFBFHx31rq5aKiqJVQMCwJCRA7vxhSU2DCggJ6Pl+PvlI3rvvvpMnyPHe+86Dh4cHzMzMYG5ujgEDBmDnzp3N+sxNrQGpqqrC/Pnz0bdvX1hbW0MgEMDNzQ1z5syBQvHsLcqm+6kIaSEHb19MWL4au1etgFe3/uDepumEllJy1cYOocM5e8YELw1xgkp13eDnNhX2hJ//ZzAX92qX/jds2ACRSIQxY8ZALBZj+PDh2LRpE5YsWYIuXVr+a6i+vh7h4eG4desWpk+fDk9PT1RWVuKnn37CoUOHMHXqVLz44otYuXIl4uLiMGrUKERGRgIAbG11p0pXrVqF8vJyTJ06Fba2tnB0vL8eZ+fOnfjll1/wxhtvwMHBAWVlZdi4cSMiIyORkZGB8ePHtzjuW7duITU1Fa+99hqioqLA4/GQl5eHxMREnD17Fvv27Wtxn50ZJSCEtIK4myVeX7AUldk3UF18E6DZlxZRghKQP2IMKCvrD7HYsAmIpeUL8PVZDRMTSbv0r1KpkJGRgdGjR0Msvl89dfLkydi2bRuysrIwfPjwFvd58eJF5OfnIzExEbNmNX17sEwmw8iRIxEXFwd/f39MmDChyXZFRUXIz8+HtbW1zvb58+dj2bJlOtveffdd9O7dG4sXL25VAiKTyVBUVKSTdMXGxuJvf/sbFi9ejBMnTiAkJKTF/XZWNAVDSCtxeTxYvOwC62hfcMUmxg6nU1Foao0dQod07qwJBAJng5yLyxXA3e1DBAaktVvyAdwfSSgvL8fkyZO12yIiImBnZ9fqxagSyf14Dxw4gOLi4ieKb+LEiXrJBwCIRCLt1wqFAmVlZVAoFAgLC8OlS5dQWVnZ4nPx+Xxt8lFfX4+KigrcvXsXQ4YMAQAcP368lZ+ic6IEhJAnJPToBtv3noPAzcLYoXQainpKQJrSOArS3szFPggO/gY9e04Bh8Np13OlpqZCKpXCwcEBBQUFKCgowLVr1xAeHo6srCzcuXOnxX06OTlhwYIF2L9/P+zt7fHcc89h1qxZOHbsWIv7cnd3b3J7SUkJYmJiYGtrC5FIBGtra0ilUqxfvx7A/TtsWmPdunXw9/eHQCCApaUlpFKpdl1KRUVFq/rsrCgBIaQN8Mz5sJ7iC8krLuCY0I/V4zzrZdgf5dzZLhAIXNqlbw6HB2enGQgK2g6xqOlfvG2psLAQOTk5KC0thYeHB9zd3bWvTZs2ob6+Hhs3bvwttocnQvX1+ouWFy5ciIKCAqxZswYeHh5IS0tD//798e6777YoRjMzM71tGo0G4eHh2LhxIyZOnIgvv/wSe/fuRXZ2tnbqRaNp+dqvpKQkxMbGws7ODikpKdizZw+ys7O1dVFa02dnRmtACGkjHC4H5i86wNTbCuXbL0N9TW7skDqsGuWzt+K/uRgDyu72h9j8Wpv2a2rqDB/vTyCR9G7Tfh8lLS0NjDGkpKTA0lL/sQaLFi3C559/jtmzZ2v3l5eX67W7evVqk/27uLggNjYWsbGxUKlUGDFiBJKTkxEXFwcXF5dWj+78+OOP+OGHH7BgwQIsXLhQZ9+GDRta1ScAfPHFF3B2dsZ3332nc3vu3r17W91nZ0YJCCFtrIu1KaQxfqg5cQfyrGtgqgZjh9ShNHRhqFXRFMyjnD3LxZBwGVSqpn/xtlSPHlFwd5sLHs+0TfprDo1Gg/T0dHh7eyMmJqbJNleuXMGcOXNw+PBhDBw4EN27d8eBAwfAGNMmD1evXsXXX3+tc1xjVVUTk9/XXgkEAvj4+GDfvn0oLy+Hi4uLdtFrS6c2eL/V+mFMd3X5Tz/91OzbcB/WL4fD0em3vr4ey5cvb3WfnRklIIS0Aw6HA3FfOwh7WeLejsuozX+25nYfRWXGQDfBPA4Hd0v7wbzrkyUgAkF3ePVaCiur0DaKq/mys7Nx48YNLFiw4KFtRo8ejTlz5iA1NRUDBw7EzJkzMX/+fAwbNgwjR47Er7/+ivXr18PX1xcnT57UHpebm4uYmBiMHj0aHh4eMDc3x7lz55CSkgJ/f38EBgYCAKysrODq6oqtW7fCzc0NUqkUNjY2CAsLe2TsXl5e8PHxQWJiIhQKBTw9PfHLL78gJSUFvr6+OHPmTKuuyZgxYzB37lwMGzYMkZGRqKysRGZmpk4i9SyhBISQdtRFIoB1tC9qzpZA/u0VaBRUgEtl2kAJSDOcO8fFkHBXqFRXWnwsh8NHz55vwcV5Bng8/TUOhtB4h8uYMWMe2sbNzQ3+/v7Ytm0b1qxZg9mzZ0Mul2Pz5s04ePAgvL29kZqaitOnT+skIAEBAYiMjEReXh4yMjLQ0NAAR0dHfPDBB5g1a5Z2BAMANm/ejLi4OCQkJKC2thahoaGPTUB4PB727NmDDz74ABs3bkRNTQ18fX2xceNGnD9/vtUJyKxZs8AYQ2pqKt577z10794dY8eORXR0NLy9vVvVZ2fGYX8cYyKEtIuGajXu7boC5Q93jR2KUd3uWYs9JUeMHUanEBDQgK6SzBYdY2UVCg/3v8HMrH0WshLSVmi5PiEGwhPzYTXeC9IYP5g4iI0djtFQGfbmO3+eC6HArVltTYU94e+XgsCAzyn5IJ0CTcEQYmACmQVsYgOhPFcK+b5CNNxTGTskg6rl1Rk7hE6Eg+LiEEgsCh7agssVwsnpbTj1jAGPJzBgbIQ8GRoBIcQIOBwOzHrboPv7Qej6sjM4grZ9+FdHpsCzlXA9qR9+4EIoaLpmh1QagX5990Pm8g4lH6TToREQQoyIY8JF10GOEAXZojLnBmqO3wE0T/eyLKWGEpCW4eBOcTAsLC5rt3Sz6AeZazwsJH2MGBchT4YWoRLSgdSVKiD/rhC1F8uMHUq72e+UjxvFN40dRqcTHn4cAoEIMlk8LC2fN3Y4hDwxGgEhpAMxkZrBeqI31L9WoyrvJpQ/lgJPWXXmGhVVQW0pBwcHODi8BicnH2OHQkiboREQQjqw+jIlqg7dRM3pEqD+6chEvrA4gtpaqoTaHE5OTnjxxRfh6upq7FAIaXOUgBDSCTRUqVF95Baqj90Gq+28pd0beAxpJgeMHUaHxuFw4ObmhgEDBsDZ2dnY4RDSbigBIaQT0dTWo/rYbVQfuQVNVee7nVXZtQEZ6oPGDqNDMjU1Re/evREUFNTkg9sIedpQAkJIJ8TqNVCcL0XNiTtQX680djjNds+2Hl/J84wdRodiZ2eHkJAQ+Pr6PrPPBCHPJlqESkgnxOnChaiPLUR9bFF3pwY1J+6g5kwJWG3HrjJaK+jY8RkKj8eDj48PgoOD4ejoaOxwCDEKGgEh5CnB6hqgvFCGmtPFUBXcAzrgT3ahrBrf/3rc2GEYja2tLfz8/NC7d2+IRCJjh0OIUdEICCFPCY4JD2aBNjALtEG9XAXF2RIoThejvlRp7NC0lM9gGXZra2v4+vrCx8cHUqnU2OEQ0mHQCAghT7m64hooL5aj9mIZ1DerjDoycsa9GGeKfjJeAAZiaWmpTTpsbW2NHQ4hHRKNgBDylDOxFcHEVoSugx3RUKWG8lIZai+Wo7bgnsFriyjY01v/w9LSEl5eXvD19YWdnZ2xwyGkw6MREEKeURp1A1S/VEB5sQy1P5dDo2j/BaJPUxl2sVgMFxcXyGQyuLi4wMLCwtghEdKp0AgIIc8oLp8HU19rmPpagzGG+mIFVNcroS6shKpQjoaKtn9onELdcdajtJRAIICTkxNkMhlkMhlsbGyMHRIhnRolIIS0kfT0dERHRyM3NxeDBg0ydjgtwuFwYNJdBJPuIqDv/emDBrlKJyGpu1PzxM+lqantPM+BkUgksLe3h729PZydnWFvbw8ej2fssAh5alACQjq8kpISfPLJJ8jKysL169fB5XJhY2ODPn364PXXX0dkZKSxQ3wq8SQCmPlLYeZ//84Njaoe6qIq1N1RoL5Ugbri+39qapo3daPhMiiVHW8EhMvlwsrKCra2trCxsYGdnR3s7e3pNllC2hklIKRDKyoqQnBwMKqqqhAVFYXp06cDAAoKCrBnzx5UV1dTAmIgXEEXCN26QejWTWd7Q00d6ksUqCtRPPCnEg1VKp0RE5WIAUa6C5fP50MikWhfFhYWkEgkkEqlkEql6NKF/ikkxNDop450aP/4xz9QXFyMXbt24c9//rPOvpUrV+LmzadjQWNnxhOZgOcigcBForOdaRg01Wo0VN5/KWoUGFTDoFAodF5qtRoajQYNDQ1oaGjQ+/pBHA4HfD4ffD4fAoFA+/WD7wUCgU6yIZFIYGZmZshLQghpDkZIBxYREcEAsKqqqke2e+utt5hAIGBKpVK77fjx4wwAE4vFTK1Wa7fn5eUxACw1NVWnj61bt7IBAwYwsVjMTE1NWUhICNu2bZveuTQaDUtMTGQymYzx+Xzm7u7O1qxZw9LS0hgAlpubq9P+3r17LCEhgbm6ujI+n8+sra3ZuHHj2JUrV3TaNR6fk5PDli9fzlxcXLT9p6enN/eSPXXq6+uZWq3W+TskhHR+XKNmP4Q8hkwmAwD8+9//BnvEHeNhYWFQqVQ4cuSIdtuBAwfA5XJRXV2NEydO6GxvPKbR/PnzMW7cOJibm+Pjjz/GihUrIBKJ8Nprr2Ht2rU654qPj0dCQgJsbW2xYsUKjB8/HkuXLkVycrJeXHK5HM8//zzWrVuHV199FcnJyZg5cyZyc3PRt29fXL9+Xe+YuXPnIjMzE2+//TZWrFgBLpeLyZMn63y2ZwmPx4OJiQk9qI2Qp42xMyBCHuXKlSusa9euDABzdHRk48ePZytXrmSnTp3SaXf79m0GgM2dO1e7LTw8nA0bNoxZWVmxhQsXare/8MILTCaTad+fOnWKAWBz5szRO/+IESOYubk5q6ysZIwx9vPPPzMOh8MGDhyo8z/ywsJCZmZmpjcC8s477zChUMjOnTun029hYSEzNzdnkyZN0m5rHAEJDAxkKpVKu/3mzZuMz+ezcePGNfOqEUJIx0cjIKRDk8lkOH/+PGbMmAGNRoPMzEzExcUhKCgI/v7+OH36NACge/fu8PLyQk5ODgBArVbjyJEjCA8Px+DBg7XbFQoFjh8/rjP6kZmZCQCYOHEi7t69q/MaPnw4qqqqcPToUQDArl27wBjD+++/r/M/cicnJ0RFRenEzhhDZmYmBgwYgB49euj0KxKJ0K9fP+zfv1/vM8+YMQN8Pl/7vkePHvDw8MDly5fb4pISQkiHQAkI6fCcnZ2xdu1a3Lx5E7/++iu2b9+O4cOH48cff8Sf/vQnlJeXA7g/pXL69GnI5XIcO3YMCoUCYWFhCAsL074/fPgw1Go1XnrpJW3/ly5dAgB4e3tr74pofL311lsAgOLiYgDAlStXAABeXl56cXp7e+u8Ly0tRVlZGXJycvT6lUqlyM7O1vb7oMZppwdZWVmhrKysNZePPGXS09PB4XBw8OBBY4dCyBOhu2BIp2JnZ4fIyEhERkZi/Pjx2LJlC7KysjBhwgSEhYVh7dq1yMvLw5kzZ2BtbQ1/f38IhUKo1WocPnxYu/5j8ODB2j7Zb2tLsrKyHrrOwMfHR+c9h8N5bKyN/Q4ePBjz5s1r9md8WLErRk9NMIqDBw9qv19iY2Pxz3/+U69NSUkJHBwcUFdXh9DQUEoOCGkGSkBIp9W/f39s2bIFt27dAnD/Fz2Xy0VOTg7OnDmDsLAwcDgceHp6okePHsjJycGBAwf0nlDq4eGBvXv3wsHBAX5+fo88p6urKwDg4sWL8PDw0Nl38eJFnfdSqRQWFhaQy+UYMmRIW3xkYkRCoRCZmZlISkqCQCDQ2bd582YwxgxST+TNN9/EuHHjdKbpCOmMaAqGdGi5ublNVs/UaDT49ttvAfw+9dGtWzcEBAQgKysLJ06c0JlmCQsLwzfffIOzZ8/qbAeACRMmAADmzZuH+nr9qp4lJSXar4cPHw4Oh4OkpCTU1f1eVev69evIyMjQOY7L5SIqKgpnzpzB1q1bm/x8D/ZNOrZRo0ahoqIC33zzjd6+tLQ0vPLKK3qJSXvg8XgQCoXgcumfb9K50Xcw6dCSkpJgb2+PN998E6tWrUJaWhqWLVuGkJAQZGdnY/DgwXj11Ve17cPCwlBQUKC3ziMsLAz5+floaGjQWYAKAMHBwVi4cCF2796NwMBALFq0CBs2bMDHH3+MkSNHwsHBQdvW09MTf/3rX3H48GGEhoZi9erVWLRoEfr164devXrpxb9kyRIEBgZi/PjxGDt2LFatWoV169Zh9uzZ8PPzQ0JCQjtcNdIe/P398dxzzyEtLU1n+4kTJ3DhwgVER0c3edypU6cwatQoWFtbQyAQwNPTE0uWLNFJdhMSEsDhcPT6vnTpEkQiEQYMGKBt/7A1IGq1GomJiQgMDISZmRkkEgmCgoL0poxu3LiByZMnw87ODnw+H87OzoiPj4dcLm/tpSGkdYx5Cw4hj3P06FEWHx/PgoKCmI2NDevSpQuTSCSsX79+LCkpidXW1uq0z8rKYgBYz549dbbfuHGDAWA8Ho9VVFQ0ea7du3ezoUOHsm7dujE+n88cHBzYyy+/zNatW6fTrrEQ2YOFwlavXv3QQmQ1NTVs0aJFzNfXlwmFQiYWi1mvXr3YX/7yF3bs2DFtu4cdzxhjoaGhzMnJqdnXjbSd3NxcBoAtW7aMJScnMy6Xy4qKirT7p02bxmxsbFhdXR0TiUQsNDRUu2/Pnj2Mz+czb29vtnTpUrZ+/Xo2adIkxuVy2ZgxY7Tt1Go169u3LxOJROzSpUuMMcaUSiXz8/NjFhYWrLCwUNu2qe8TlUrFBg0axACwiIgI9sknn7Dk5GQWExPDBg8erG1348YNZmtry0xMTNg777zD1q5dyyZMmMAAsICAAKZQKNrhChLSNEpACCHkER5MQMrKyphAIGBLlixhjDGmUCiYRCJh77//PmOM6SQgSqWS2djYsBdeeIHV1dXp9Pnpp5/qJRHXrl1jFhYWLCAggNXW1rJp06YxAGz79u06xzaVgKxYsYIBYB9++KFe/A0NDdqvo6KiGAD29ddf67RZvHgxA6D9XIQYAk3BEEJIM1laWmLEiBFIT08HAOzYsQNyuRxTpkzRa5udnY2SkhJMnDgR9+7d06kD88orrwCATh0YZ2dnbNiwAefPn8dLL72ElJQUTJ8+vVkPW8zIyIBEIsH8+fP19jWuFdFoNNi1axf8/PwwYsQInTbx8fEQi8XYsWNHs68FIU+KEhBCCGmB6OhoXL58GYcPH8bnn3+OkJAQvRowwO/1ZaZOnapXA6ZxvdAf68CMHj0akyZNwpEjR+Dl5YVPP/20WTFdvnwZnp6eEAqFD21TWlqKqqoqvVvKAcDU1BSurq64evVqs85HSFug23AJIaQFhg4dCgcHByxcuBC5ubn47LPPmmzHfqvbsnz5cvTp06fJNvb29jrv5XI5Dh06BAC4ffs2iouL4eTk1CZxs8fUkXncfkLaGiUghBDSAlwuFxMnTsTSpUthamqKcePGNdmusU6MmZlZs+vAxMTE4Pr160hOTkZCQgLeeOMNHDp06LH1RTw8PJCfn4/a2tqHjoLY2NjA3NwcFy5c0NtXW1uLq1evNnknFyHthaZgCCGkhaZNm4aPPvoI69evh0QiabJNREQEbGxskJiYiLt37+rtVyqVqKqq0r5PSUnBf/7zH8ydOxczZ87E6tWrcfToUfz9739/bDxRUVGQy+VYvHix3r7GkQ0ul6t9hMHu3bt12qxatQrV1dXNWm9CSFvhMBp3I4SQh2osxb5s2TLMmTPnkW3FYjGCgoK0NTr27duHkSNHQiQSYcqUKXB3d8e9e/fw888/Y8eOHdi5cycGDRqECxcuIDg4GL1790ZeXp52xGPs2LH46quv8P3332vLwaenpyM6Ohq5ubkYNGgQgPs1QIYMGYL//ve/iIiIwNChQyEUCnHhwgXk5+fj+++/BwAUFRUhODgYFRUVePvtt9GrVy8cO3YMmzZtQkBAAP73v//BzMysfS4kIX9AUzCEENJOIiIicPLkSSxfvhwZGRkoLS1Ft27d4Orqivj4ePj7+0OpVGLs2LEQCoXYsmWLznTLv/71L5w8eRITJkzA+fPnYW1t3eR5+Hw+9u/fj6SkJGRmZmLevHkQCoVwd3fXKZDm6OiI48ePY8GCBfjyyy9RXl4OOzs7xMXF4aOPPqLkgxgUjYAQQgghxOBoDQghhBBCDI4SEEIIIYQYHCUghBBCCDE4SkAIIYQQYnCUgBBCCCHE4CgBIYQQQojBUQJCCCGEEIOjBIQQQgghBkcJCCGEEEIMjhIQQgghhBgcJSCEEEIIMThKQAghhBBicJSAEEIIIcTgKAEhhBBCiMFRAkIIIYQQg6MEhBBCCCEGRwkIIYQQQgyOEhBCCCGEGBwlIIQQQggxOEpACCGEEGJwlIAQQgghxOAoASGEEEKIwVECQgghhBCDowSEEEIIIQZHCQghhBBCDI4SEEIIIYQYHCUghBBCCDE4SkAIIYQQYnD/B5INx+JbOpLpAAAAAElFTkSuQmCC"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "9e2ffb53",
+   "metadata": {},
+   "source": [
+    "### Task 3.4 Display a Pie Chart of the average overall score of the top 10 countries in descending order\n",
+    "\n",
+    "Your plot should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:pie_plot.jpg\" width=\"400\"/></div>\n",
+    "\n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aedb58d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have done this one for you\n",
+    "\n",
+    "def pie_plot(df, x, y, title=None):\n",
+    "    \"\"\"pie_plot(df, x, y, title) takes in a DataFrame 'df' and displays \n",
+    "    a pie plot with the column 'x' as the x-axis, the (numeric) column\n",
+    "    'y' as the y-axis, and the 'title' as the title of the plot\"\"\"\n",
+    "    df = df.set_index(x)\n",
+    "    ax = df.plot.pie(y=y, legend=False)\n",
+    "    ax.set_ylabel(None)\n",
+    "    ax.set_title(title)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "805c89c1",
+   "metadata": {},
+   "source": [
+    "Use the `pie_plot` function to create the required plot.\n",
+    "\n",
+    "* Write a SQL query to select the **top** *10* countries based on **average** `Overall`.\n",
+    "* Save the resulting **DataFrame** with **two** columns: `Country`, and the **average** of the `Overall`.\n",
+    "* Call `pie_plot`, passing in `Country` as the `x` argument, and the **average** of the `Overall` as the `y` argument.\n",
+    "* Your plot must also have the **title** `Countries with top 10 Overall` as in the image.\n",
+    "\n",
+    "**Hint:** If you are having trouble writing the SQL query, take a look at Task 2.3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "777d3b49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame\n",
+    "# then create the pie plot using the DataFrame\n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "attachments": {
+    "regression_line_plot.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c89e111c",
+   "metadata": {},
+   "source": [
+    "### Task 3.5: Fit a regression line to the data from Task 3.2\n",
+    "\n",
+    "Your line of best fit should look like this:\n",
+    "\n",
+    "<div><img src=\"attachment:regression_line_plot.jpg\" width=\"500\"/></div>\n",
+    "    \n",
+    "Make sure that the plot is labelled exactly as in the image here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68941bde",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we have defined this function for you\n",
+    "\n",
+    "def get_regression_coeff(df, x, y):\n",
+    "    \"\"\"get_regression_coeff(df, x, y) takes in a DataFrame 'df' and returns \n",
+    "    the slope (m) and the y-intercept (b) of the line of best fit in the\n",
+    "    plot with the column 'x' as the x-axis, and the column 'y' as the y-axis\"\"\"\n",
+    "    df[\"1\"] = 1\n",
+    "    res = np.linalg.lstsq(df[[x, \"1\"]], df[y], rcond=None)\n",
+    "    coefficients = res[0]\n",
+    "    m = coefficients[0]\n",
+    "    b = coefficients[1]\n",
+    "    return (m, b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb427287",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you must define this function to compute the best fit line\n",
+    "\n",
+    "def get_regression_line(df, x, y):\n",
+    "    \"\"\"get_regression_line(df, x, y) takes in a DataFrame 'df' and returns \n",
+    "    a DataFrame with an additional column \"fit\" of the line of best fit in the\n",
+    "    plot with the column 'x' as the x-axis, and the column 'y' as the y-axis\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: use the 'get_regression_coeff' function to get the slope and\n",
+    "    #       intercept of the line of best fit\n",
+    "    # TODO: save them into variables m and b respectively\n",
+    "    \n",
+    "    # TODO: create a new column in the dataframe called 'fit', which is\n",
+    "    #       calculated as df['fit'] = m * df[x] + b\n",
+    "    \n",
+    "    # TODO: return the DataFrame df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a70404d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# you must define this function to plot the best fit line on the scatter plot\n",
+    "\n",
+    "def regression_line_plot(df, x, y):\n",
+    "    \"\"\"regression_line_plot(df, x, y) takes in a DataFrame 'df' and displays\n",
+    "    a scatter plot with the column 'x' as the x-axis, and the column\n",
+    "    'y' as the y-axis, as well as the best fit line for the plot\"\"\"\n",
+    "    pass # replace with your code\n",
+    "    # TODO: use 'get_regression_line' to get the data for the best fit line.\n",
+    "    \n",
+    "    # TODO: use df.plot.scatter (not scatter_plot) to plot the x and y columns\n",
+    "    #       of 'df' in black color.\n",
+    "    # TODO: save the return value of df.plot.scatter to a variable called 'ax'\n",
+    "    \n",
+    "    # TODO: use df.plot.line to plot the fitted line in red,\n",
+    "    #       using ax=ax as a keyword argument.\n",
+    "    #       this ensures that both the scatter plot and line end up on the same plot\n",
+    "    #       play careful attention to what the 'x' and 'y' arguments ought to be"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef4b46de",
+   "metadata": {},
+   "source": [
+    "Now, use the `regression_line_plot` function to create the required plot.\n",
+    "\n",
+    "* Call `regression_line_plot` on your data from Task 3.2 to show the correlation between `Employer Reputation` and `Academic Reputation`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "065d0ef5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create the scatter plot with the best fit line using the DataFrame from Task 3.2 \n",
+    "# verify that this plot matches exactly with the image shown above\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bdb5cdb7",
+   "metadata": {},
+   "source": [
+    "### Task 4: Closing the connection\n",
+    "\n",
+    "Now that you are done with your database, it is very important to close it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65557b40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# close your connection here\n",
+    "\n",
+    "# we have done this one for you\n",
+    "conn.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f20a99c",
+   "metadata": {},
+   "source": [
+    "### Congratulations, you are now ready to start P13!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p13/README.md b/p13/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..2358531f7a4a299b0eada2b6578146827a6b6804
--- /dev/null
+++ b/p13/README.md
@@ -0,0 +1,41 @@
+# Project 13 (P13): World University Rankings
+
+
+## Corrections and clarifications:
+
+* None yet.
+
+**Find any issues?** Report to us:
+
+- Ashwin Maran <amaran@wisc.edu>
+
+
+## Instructions:
+
+This project will focus on **SQL**, and **Plotting**. To start, download [`p13.ipynb`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p13/p13.ipynb), [`public_tests.py`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p13/public_tests.py), and [`expected_dfs.html`](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p13/expected_dfs.html).
+
+**Important Warning:** You must **not** manually download any of the other files. In particular, you are **not** allowed to manually download the file `rankings.json`. You **must** download this files using Python in your `p13.ipynb` notebook as a part of the project. Otherwise, your code may pass on **your computer**, but **fail** on the testing computer.
+
+You will work on `p13.ipynb` and hand it in. You should follow the provided directions for each question. Questions have **specific** directions on what **to do** and what **not to do**.
+
+------------------------------
+
+## IMPORTANT Submission instructions:
+- Review the [Grading Rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/p13/rubric.md), to ensure that you don't lose points during code review.
+- Login to [Gradescope](https://www.gradescope.com/) and upload the zip file into the P13 assignment.
+- If you completed the project with a **partner**, make sure to **add their name** by clicking "Add Group Member"
+in Gradescope when uploading the P13 zip file.
+
+   <img src="images/add_group_member.png" width="400">
+
+   **Warning:** You will have to add your partner on Gradescope even if you have filled out this information in your `p13.ipynb` notebook.
+
+- It is **your responsibility** to make sure that your project clears auto-grader tests on the Gradescope test system. Otter test results should be available within forty minutes after your submission (usually within ten minutes). **Ignore** the `-/100.00` that is displayed to the right. You should be able to see both PASS / FAIL results for the 20 test cases, which is accessible via Gradescope Dashboard (as in the image below):
+
+    <img src="images/gradescope.png" width="400">
+
+- You can view your **final score** at the **end of the page**. If you pass all tests, then you will receive **full points** for the project. Otherwise, you can see your final score in the **summary** section of the test results (as in the image below):
+
+   <img src="images/summary.png" width="400">
+
+   If you want more details on why you lost points on a particular test, you can scroll up to find more details about the test.
diff --git a/p13/expected_dfs.html b/p13/expected_dfs.html
new file mode 100644
index 0000000000000000000000000000000000000000..4289cf091a6245b0f7a5df40ded4dea795bf9889
--- /dev/null
+++ b/p13/expected_dfs.html
@@ -0,0 +1,24019 @@
+<h1>Data Frame q1</h1>
+<table border="1" class="dataframe" data-question="q1">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Year
+            </th>
+            <th>
+                Rank
+            </th>
+            <th>
+                Institution Name
+            </th>
+            <th>
+                Country
+            </th>
+            <th>
+                Academic Reputation
+            </th>
+            <th>
+                Employer Reputation
+            </th>
+            <th>
+                Faculty Student
+            </th>
+            <th>
+                Citations per Faculty
+            </th>
+            <th>
+                International Faculty
+            </th>
+            <th>
+                International Students
+            </th>
+            <th>
+                International Research Network
+            </th>
+            <th>
+                Employment Outcomes
+            </th>
+            <th>
+                Sustainability
+            </th>
+            <th>
+                Overall
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                2022
+            </td>
+            <td>
+                75
+            </td>
+            <td>
+                University of Wisconsin-Madison
+            </td>
+            <td>
+                United States
+            </td>
+            <td>
+                83.4
+            </td>
+            <td>
+                52.8
+            </td>
+            <td>
+                69.2
+            </td>
+            <td>
+                58.4
+            </td>
+            <td>
+                8.1
+            </td>
+            <td>
+                27.5
+            </td>
+            <td>
+                NaN
+            </td>
+            <td>
+                NaN
+            </td>
+            <td>
+                NaN
+            </td>
+            <td>
+                66.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                2023
+            </td>
+            <td>
+                83
+            </td>
+            <td>
+                University of Wisconsin-Madison
+            </td>
+            <td>
+                United States
+            </td>
+            <td>
+                82.4
+            </td>
+            <td>
+                48.1
+            </td>
+            <td>
+                70.6
+            </td>
+            <td>
+                41.9
+            </td>
+            <td>
+                37.7
+            </td>
+            <td>
+                23.8
+            </td>
+            <td>
+                93.2
+            </td>
+            <td>
+                84.6
+            </td>
+            <td>
+                NaN
+            </td>
+            <td>
+                63.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                2024
+            </td>
+            <td>
+                102
+            </td>
+            <td>
+                University of Wisconsin-Madison
+            </td>
+            <td>
+                United States
+            </td>
+            <td>
+                80.2
+            </td>
+            <td>
+                47.8
+            </td>
+            <td>
+                61.3
+            </td>
+            <td>
+                37.4
+            </td>
+            <td>
+                30.9
+            </td>
+            <td>
+                22.8
+            </td>
+            <td>
+                83.6
+            </td>
+            <td>
+                73.1
+            </td>
+            <td>
+                83.7
+            </td>
+            <td>
+                60.0
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q2</h1>
+<table border="1" class="dataframe" data-question="q2">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Institution Name
+            </th>
+            <th>
+                International Students
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                Tokyo Institute of Technology (Tokyo Tech)
+            </td>
+            <td>
+                31.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                The University of Tokyo
+            </td>
+            <td>
+                29.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                Waseda University
+            </td>
+            <td>
+                28.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Kyushu University
+            </td>
+            <td>
+                25.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                Hitotsubashi University
+            </td>
+            <td>
+                22.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                University of Tsukuba
+            </td>
+            <td>
+                21.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                Kyoto University
+            </td>
+            <td>
+                20.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                Nagoya University
+            </td>
+            <td>
+                19.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                Hokkaido University
+            </td>
+            <td>
+                14.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                Tohoku University
+            </td>
+            <td>
+                13.8
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q3</h1>
+<table border="1" class="dataframe" data-question="q3">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Institution Name
+            </th>
+            <th>
+                Reputation
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                Harvard University
+            </td>
+            <td>
+                200.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                Massachusetts Institute of Technology (MIT)
+            </td>
+            <td>
+                200.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                Stanford University
+            </td>
+            <td>
+                200.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                University of California, Berkeley (UCB)
+            </td>
+            <td>
+                200.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                University of California, Los Angeles (UCLA)
+            </td>
+            <td>
+                199.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                Yale University
+            </td>
+            <td>
+                199.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                Princeton University
+            </td>
+            <td>
+                198.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                Columbia University
+            </td>
+            <td>
+                197.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                New York University (NYU)
+            </td>
+            <td>
+                194.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                University of Chicago
+            </td>
+            <td>
+                191.4
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q4</h1>
+<table border="1" class="dataframe" data-question="q4">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Country
+            </th>
+            <th>
+                Number of Institutions
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                United States
+            </td>
+            <td>
+                87
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                United Kingdom
+            </td>
+            <td>
+                49
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                Germany
+            </td>
+            <td>
+                31
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Australia
+            </td>
+            <td>
+                26
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                China (Mainland)
+            </td>
+            <td>
+                26
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                Russia
+            </td>
+            <td>
+                17
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                Canada
+            </td>
+            <td>
+                16
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                Japan
+            </td>
+            <td>
+                16
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                South Korea
+            </td>
+            <td>
+                16
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                Italy
+            </td>
+            <td>
+                14
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q5</h1>
+<table border="1" class="dataframe" data-question="q5">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Country
+            </th>
+            <th>
+                Number of Institutions
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                United States
+            </td>
+            <td>
+                87
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                United Kingdom
+            </td>
+            <td>
+                49
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                Germany
+            </td>
+            <td>
+                31
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Australia
+            </td>
+            <td>
+                26
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                China (Mainland)
+            </td>
+            <td>
+                26
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                Russia
+            </td>
+            <td>
+                17
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                Canada
+            </td>
+            <td>
+                16
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                Japan
+            </td>
+            <td>
+                16
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                South Korea
+            </td>
+            <td>
+                16
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                Italy
+            </td>
+            <td>
+                14
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                Other
+            </td>
+            <td>
+                202
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q6</h1>
+<table border="1" class="dataframe" data-question="q6">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Country
+            </th>
+            <th>
+                Total Score
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                United States
+            </td>
+            <td>
+                4441.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                United Kingdom
+            </td>
+            <td>
+                2543.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                Australia
+            </td>
+            <td>
+                1243.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Germany
+            </td>
+            <td>
+                1235.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                China (Mainland)
+            </td>
+            <td>
+                1138.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                Japan
+            </td>
+            <td>
+                796.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                Canada
+            </td>
+            <td>
+                785.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                South Korea
+            </td>
+            <td>
+                739.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                Netherlands
+            </td>
+            <td>
+                673.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                Russia
+            </td>
+            <td>
+                582.6
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q7</h1>
+<table border="1" class="dataframe" data-question="q7">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Institution Name
+            </th>
+            <th>
+                International Score
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                Massachusetts Institute of Technology (MIT)
+            </td>
+            <td>
+                188.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                Rice University
+            </td>
+            <td>
+                185.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                California Institute of Technology (Caltech)
+            </td>
+            <td>
+                181.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Yale University
+            </td>
+            <td>
+                168.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                University of Pennsylvania
+            </td>
+            <td>
+                166.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                University of Chicago
+            </td>
+            <td>
+                165.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                University of Rochester
+            </td>
+            <td>
+                163.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                University of California, Berkeley (UCB)
+            </td>
+            <td>
+                156.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                Johns Hopkins University
+            </td>
+            <td>
+                155.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                Northeastern University
+            </td>
+            <td>
+                154.5
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q8</h1>
+<table border="1" class="dataframe" data-question="q8">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Citations per Faculty
+            </th>
+            <th>
+                Overall
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                100.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                92.3
+            </td>
+            <td>
+                99.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                90.6
+            </td>
+            <td>
+                98.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                98.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                99.9
+            </td>
+            <td>
+                98.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                94.0
+            </td>
+            <td>
+                97.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                98.9
+            </td>
+            <td>
+                93.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                93.2
+            </td>
+            <td>
+                92.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                74.3
+            </td>
+            <td>
+                92.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                99.9
+            </td>
+            <td>
+                90.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                78.5
+            </td>
+            <td>
+                90.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                11
+            </th>
+            <td>
+                68.4
+            </td>
+            <td>
+                89.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                12
+            </th>
+            <td>
+                99.1
+            </td>
+            <td>
+                89.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                13
+            </th>
+            <td>
+                92.1
+            </td>
+            <td>
+                87.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                14
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                87.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                15
+            </th>
+            <td>
+                45.8
+            </td>
+            <td>
+                87.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                16
+            </th>
+            <td>
+                97.5
+            </td>
+            <td>
+                87.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                17
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                87.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                18
+            </th>
+            <td>
+                95.9
+            </td>
+            <td>
+                86.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                19
+            </th>
+            <td>
+                93.5
+            </td>
+            <td>
+                86.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                20
+            </th>
+            <td>
+                57.2
+            </td>
+            <td>
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+            </td>
+            <td>
+                25.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                457
+            </th>
+            <td>
+                73.1
+            </td>
+            <td>
+                25.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                458
+            </th>
+            <td>
+                18.4
+            </td>
+            <td>
+                25.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                459
+            </th>
+            <td>
+                45.1
+            </td>
+            <td>
+                25.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                460
+            </th>
+            <td>
+                25.6
+            </td>
+            <td>
+                25.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                461
+            </th>
+            <td>
+                38.6
+            </td>
+            <td>
+                25.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                462
+            </th>
+            <td>
+                30.7
+            </td>
+            <td>
+                24.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                463
+            </th>
+            <td>
+                10.9
+            </td>
+            <td>
+                24.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                464
+            </th>
+            <td>
+                10.0
+            </td>
+            <td>
+                24.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                465
+            </th>
+            <td>
+                14.8
+            </td>
+            <td>
+                24.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                466
+            </th>
+            <td>
+                70.8
+            </td>
+            <td>
+                24.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                467
+            </th>
+            <td>
+                14.9
+            </td>
+            <td>
+                24.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                468
+            </th>
+            <td>
+                21.4
+            </td>
+            <td>
+                24.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                469
+            </th>
+            <td>
+                13.5
+            </td>
+            <td>
+                24.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                470
+            </th>
+            <td>
+                52.7
+            </td>
+            <td>
+                24.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                471
+            </th>
+            <td>
+                48.5
+            </td>
+            <td>
+                24.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                472
+            </th>
+            <td>
+                51.0
+            </td>
+            <td>
+                24.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                473
+            </th>
+            <td>
+                9.6
+            </td>
+            <td>
+                24.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                474
+            </th>
+            <td>
+                68.7
+            </td>
+            <td>
+                24.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                475
+            </th>
+            <td>
+                2.1
+            </td>
+            <td>
+                24.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                476
+            </th>
+            <td>
+                3.0
+            </td>
+            <td>
+                24.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                477
+            </th>
+            <td>
+                11.6
+            </td>
+            <td>
+                24.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                478
+            </th>
+            <td>
+                19.6
+            </td>
+            <td>
+                24.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                479
+            </th>
+            <td>
+                21.2
+            </td>
+            <td>
+                24.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                480
+            </th>
+            <td>
+                9.6
+            </td>
+            <td>
+                24.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                481
+            </th>
+            <td>
+                85.1
+            </td>
+            <td>
+                24.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                482
+            </th>
+            <td>
+                39.7
+            </td>
+            <td>
+                23.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                483
+            </th>
+            <td>
+                2.2
+            </td>
+            <td>
+                23.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                484
+            </th>
+            <td>
+                14.8
+            </td>
+            <td>
+                23.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                485
+            </th>
+            <td>
+                1.2
+            </td>
+            <td>
+                23.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                486
+            </th>
+            <td>
+                57.1
+            </td>
+            <td>
+                24.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                487
+            </th>
+            <td>
+                22.4
+            </td>
+            <td>
+                23.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                488
+            </th>
+            <td>
+                96.4
+            </td>
+            <td>
+                23.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                489
+            </th>
+            <td>
+                47.5
+            </td>
+            <td>
+                23.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                490
+            </th>
+            <td>
+                1.7
+            </td>
+            <td>
+                23.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                491
+            </th>
+            <td>
+                38.1
+            </td>
+            <td>
+                23.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                492
+            </th>
+            <td>
+                79.8
+            </td>
+            <td>
+                23.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                493
+            </th>
+            <td>
+                37.2
+            </td>
+            <td>
+                23.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                494
+            </th>
+            <td>
+                20.9
+            </td>
+            <td>
+                23.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                495
+            </th>
+            <td>
+                8.9
+            </td>
+            <td>
+                23.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                496
+            </th>
+            <td>
+                82.0
+            </td>
+            <td>
+                23.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                497
+            </th>
+            <td>
+                70.7
+            </td>
+            <td>
+                23.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                498
+            </th>
+            <td>
+                8.5
+            </td>
+            <td>
+                23.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                499
+            </th>
+            <td>
+                7.7
+            </td>
+            <td>
+                23.2
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q9</h1>
+<table border="1" class="dataframe" data-question="q9">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Academic Reputation
+            </th>
+            <th>
+                Employer Reputation
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                100.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                100.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                100.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                96.5
+            </td>
+            <td>
+                87.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                99.2
+            </td>
+            <td>
+                92.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                96.5
+            </td>
+            <td>
+                92.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                99.9
+            </td>
+            <td>
+                98.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                99.9
+            </td>
+            <td>
+                100.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                98.3
+            </td>
+            <td>
+                91.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                99.7
+            </td>
+            <td>
+                98.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                87.9
+            </td>
+            <td>
+                47.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                11
+            </th>
+            <td>
+                98.5
+            </td>
+            <td>
+                91.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                12
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                100.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                13
+            </th>
+            <td>
+                84.4
+            </td>
+            <td>
+                71.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                14
+            </th>
+            <td>
+                95.9
+            </td>
+            <td>
+                99.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                15
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                99.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                16
+            </th>
+            <td>
+                86.1
+            </td>
+            <td>
+                79.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                17
+            </th>
+            <td>
+                72.1
+            </td>
+            <td>
+                69.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                18
+            </th>
+            <td>
+                90.0
+            </td>
+            <td>
+                61.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                19
+            </th>
+            <td>
+                57.6
+            </td>
+            <td>
+                54.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                20
+            </th>
+            <td>
+                93.7
+            </td>
+            <td>
+                85.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                21
+            </th>
+            <td>
+                84.8
+            </td>
+            <td>
+                48.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                22
+            </th>
+            <td>
+                82.4
+            </td>
+            <td>
+                48.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                23
+            </th>
+            <td>
+                85.6
+            </td>
+            <td>
+                60.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                24
+            </th>
+            <td>
+                67.7
+            </td>
+            <td>
+                77.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                25
+            </th>
+            <td>
+                76.7
+            </td>
+            <td>
+                69.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                26
+            </th>
+            <td>
+                38.8
+            </td>
+            <td>
+                26.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                27
+            </th>
+            <td>
+                64.8
+            </td>
+            <td>
+                51.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                28
+            </th>
+            <td>
+                65.5
+            </td>
+            <td>
+                44.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                29
+            </th>
+            <td>
+                62.3
+            </td>
+            <td>
+                57.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                30
+            </th>
+            <td>
+                40.1
+            </td>
+            <td>
+                15.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                31
+            </th>
+            <td>
+                62.7
+            </td>
+            <td>
+                73.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                32
+            </th>
+            <td>
+                54.6
+            </td>
+            <td>
+                59.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                33
+            </th>
+            <td>
+                61.1
+            </td>
+            <td>
+                62.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                34
+            </th>
+            <td>
+                23.8
+            </td>
+            <td>
+                20.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                35
+            </th>
+            <td>
+                55.9
+            </td>
+            <td>
+                24.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                36
+            </th>
+            <td>
+                25.2
+            </td>
+            <td>
+                15.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                37
+            </th>
+            <td>
+                60.5
+            </td>
+            <td>
+                61.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                38
+            </th>
+            <td>
+                61.5
+            </td>
+            <td>
+                65.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                39
+            </th>
+            <td>
+                51.2
+            </td>
+            <td>
+                35.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                40
+            </th>
+            <td>
+                18.3
+            </td>
+            <td>
+                20.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                41
+            </th>
+            <td>
+                39.9
+            </td>
+            <td>
+                16.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                42
+            </th>
+            <td>
+                60.1
+            </td>
+            <td>
+                28.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                43
+            </th>
+            <td>
+                48.5
+            </td>
+            <td>
+                47.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                44
+            </th>
+            <td>
+                25.9
+            </td>
+            <td>
+                22.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                45
+            </th>
+            <td>
+                19.2
+            </td>
+            <td>
+                25.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                46
+            </th>
+            <td>
+                46.7
+            </td>
+            <td>
+                40.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                47
+            </th>
+            <td>
+                39.7
+            </td>
+            <td>
+                18.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                48
+            </th>
+            <td>
+                26.6
+            </td>
+            <td>
+                44.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                49
+            </th>
+            <td>
+                2.6
+            </td>
+            <td>
+                3.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                50
+            </th>
+            <td>
+                40.7
+            </td>
+            <td>
+                16.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                51
+            </th>
+            <td>
+                35.2
+            </td>
+            <td>
+                37.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                52
+            </th>
+            <td>
+                39.6
+            </td>
+            <td>
+                22.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                53
+            </th>
+            <td>
+                37.6
+            </td>
+            <td>
+                35.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                54
+            </th>
+            <td>
+                31.2
+            </td>
+            <td>
+                44.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                55
+            </th>
+            <td>
+                22.7
+            </td>
+            <td>
+                32.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                56
+            </th>
+            <td>
+                29.2
+            </td>
+            <td>
+                23.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                57
+            </th>
+            <td>
+                18.3
+            </td>
+            <td>
+                7.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                58
+            </th>
+            <td>
+                37.0
+            </td>
+            <td>
+                13.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                59
+            </th>
+            <td>
+                42.0
+            </td>
+            <td>
+                31.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                60
+            </th>
+            <td>
+                36.6
+            </td>
+            <td>
+                26.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                61
+            </th>
+            <td>
+                15.5
+            </td>
+            <td>
+                10.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                62
+            </th>
+            <td>
+                27.2
+            </td>
+            <td>
+                29.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                63
+            </th>
+            <td>
+                28.5
+            </td>
+            <td>
+                25.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                64
+            </th>
+            <td>
+                14.9
+            </td>
+            <td>
+                16.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                65
+            </th>
+            <td>
+                18.6
+            </td>
+            <td>
+                5.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                66
+            </th>
+            <td>
+                15.6
+            </td>
+            <td>
+                14.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                67
+            </th>
+            <td>
+                20.5
+            </td>
+            <td>
+                12.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                68
+            </th>
+            <td>
+                16.5
+            </td>
+            <td>
+                7.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                69
+            </th>
+            <td>
+                19.1
+            </td>
+            <td>
+                19.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                70
+            </th>
+            <td>
+                19.3
+            </td>
+            <td>
+                9.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                71
+            </th>
+            <td>
+                17.7
+            </td>
+            <td>
+                10.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                72
+            </th>
+            <td>
+                7.9
+            </td>
+            <td>
+                8.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                73
+            </th>
+            <td>
+                5.4
+            </td>
+            <td>
+                7.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                74
+            </th>
+            <td>
+                14.8
+            </td>
+            <td>
+                8.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                75
+            </th>
+            <td>
+                25.5
+            </td>
+            <td>
+                24.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                76
+            </th>
+            <td>
+                17.8
+            </td>
+            <td>
+                8.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                77
+            </th>
+            <td>
+                8.4
+            </td>
+            <td>
+                6.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                78
+            </th>
+            <td>
+                3.1
+            </td>
+            <td>
+                3.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                79
+            </th>
+            <td>
+                20.7
+            </td>
+            <td>
+                13.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                80
+            </th>
+            <td>
+                9.5
+            </td>
+            <td>
+                4.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                81
+            </th>
+            <td>
+                19.4
+            </td>
+            <td>
+                13.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                82
+            </th>
+            <td>
+                9.6
+            </td>
+            <td>
+                3.8
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q10</h1>
+<table border="1" class="dataframe" data-question="q10">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Faculty Student
+            </th>
+            <th>
+                International Students
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                76.0
+            </td>
+            <td>
+                68.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                38.0
+            </td>
+            <td>
+                96.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                24.2
+            </td>
+            <td>
+                89.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                96.8
+            </td>
+            <td>
+                48.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                7.7
+            </td>
+            <td>
+                43.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                17.4
+            </td>
+            <td>
+                2.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                96.5
+            </td>
+            <td>
+                65.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                68.9
+            </td>
+            <td>
+                95.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                22.3
+            </td>
+            <td>
+                3.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                87.3
+            </td>
+            <td>
+                36.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                19.5
+            </td>
+            <td>
+                1.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                11
+            </th>
+            <td>
+                69.4
+            </td>
+            <td>
+                4.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                12
+            </th>
+            <td>
+                5.1
+            </td>
+            <td>
+                27.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                13
+            </th>
+            <td>
+                41.3
+            </td>
+            <td>
+                51.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                14
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                24.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                15
+            </th>
+            <td>
+                40.8
+            </td>
+            <td>
+                4.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                16
+            </th>
+            <td>
+                88.9
+            </td>
+            <td>
+                24.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                17
+            </th>
+            <td>
+                66.5
+            </td>
+            <td>
+                5.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                18
+            </th>
+            <td>
+                99.8
+            </td>
+            <td>
+                83.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                19
+            </th>
+            <td>
+                96.4
+            </td>
+            <td>
+                95.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                20
+            </th>
+            <td>
+                5.3
+            </td>
+            <td>
+                7.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                21
+            </th>
+            <td>
+                84.2
+            </td>
+            <td>
+                98.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                22
+            </th>
+            <td>
+                56.3
+            </td>
+            <td>
+                1.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                23
+            </th>
+            <td>
+                62.3
+            </td>
+            <td>
+                2.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                24
+            </th>
+            <td>
+                7.7
+            </td>
+            <td>
+                1.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                25
+            </th>
+            <td>
+                25.7
+            </td>
+            <td>
+                93.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                26
+            </th>
+            <td>
+                78.8
+            </td>
+            <td>
+                7.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                27
+            </th>
+            <td>
+                5.4
+            </td>
+            <td>
+                54.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                28
+            </th>
+            <td>
+                91.9
+            </td>
+            <td>
+                27.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                29
+            </th>
+            <td>
+                98.3
+            </td>
+            <td>
+                34.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                30
+            </th>
+            <td>
+                62.1
+            </td>
+            <td>
+                61.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                31
+            </th>
+            <td>
+                79.8
+            </td>
+            <td>
+                9.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                32
+            </th>
+            <td>
+                16.4
+            </td>
+            <td>
+                98.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                33
+            </th>
+            <td>
+                67.8
+            </td>
+            <td>
+                53.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                34
+            </th>
+            <td>
+                43.2
+            </td>
+            <td>
+                2.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                35
+            </th>
+            <td>
+                26.7
+            </td>
+            <td>
+                80.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                36
+            </th>
+            <td>
+                22.4
+            </td>
+            <td>
+                92.4
+            </td>
+        </tr>
+        <tr>
+            <th>
+                37
+            </th>
+            <td>
+                83.8
+            </td>
+            <td>
+                27.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                38
+            </th>
+            <td>
+                67.3
+            </td>
+            <td>
+                3.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                39
+            </th>
+            <td>
+                77.0
+            </td>
+            <td>
+                5.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                40
+            </th>
+            <td>
+                27.7
+            </td>
+            <td>
+                1.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                41
+            </th>
+            <td>
+                46.8
+            </td>
+            <td>
+                1.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                42
+            </th>
+            <td>
+                38.4
+            </td>
+            <td>
+                9.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                43
+            </th>
+            <td>
+                12.8
+            </td>
+            <td>
+                21.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                44
+            </th>
+            <td>
+                61.1
+            </td>
+            <td>
+                94.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                45
+            </th>
+            <td>
+                99.8
+            </td>
+            <td>
+                91.9
+            </td>
+        </tr>
+        <tr>
+            <th>
+                46
+            </th>
+            <td>
+                66.3
+            </td>
+            <td>
+                54.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                47
+            </th>
+            <td>
+                79.8
+            </td>
+            <td>
+                73.5
+            </td>
+        </tr>
+        <tr>
+            <th>
+                48
+            </th>
+            <td>
+                15.7
+            </td>
+            <td>
+                30.1
+            </td>
+        </tr>
+        <tr>
+            <th>
+                49
+            </th>
+            <td>
+                87.0
+            </td>
+            <td>
+                10.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                50
+            </th>
+            <td>
+                11.8
+            </td>
+            <td>
+                20.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                51
+            </th>
+            <td>
+                70.0
+            </td>
+            <td>
+                70.2
+            </td>
+        </tr>
+        <tr>
+            <th>
+                52
+            </th>
+            <td>
+                74.2
+            </td>
+            <td>
+                98.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                53
+            </th>
+            <td>
+                33.4
+            </td>
+            <td>
+                32.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                54
+            </th>
+            <td>
+                34.6
+            </td>
+            <td>
+                2.8
+            </td>
+        </tr>
+        <tr>
+            <th>
+                55
+            </th>
+            <td>
+                31.2
+            </td>
+            <td>
+                10.6
+            </td>
+        </tr>
+        <tr>
+            <th>
+                56
+            </th>
+            <td>
+                91.7
+            </td>
+            <td>
+                69.7
+            </td>
+        </tr>
+        <tr>
+            <th>
+                57
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                96.3
+            </td>
+        </tr>
+        <tr>
+            <th>
+                58
+            </th>
+            <td>
+                100.0
+            </td>
+            <td>
+                90.0
+            </td>
+        </tr>
+        <tr>
+            <th>
+                59
+            </th>
+            <td>
+                78.9
+            </td>
+            <td>
+                3.6
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q13</h1>
+<table border="1" class="dataframe" data-question="q13">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Country
+            </th>
+            <th>
+                Sum of International Citations
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                United States
+            </td>
+            <td>
+                2294.2671
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                United Kingdom
+            </td>
+            <td>
+                2279.9530
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                Australia
+            </td>
+            <td>
+                1895.6595
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Canada
+            </td>
+            <td>
+                822.9573
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                Netherlands
+            </td>
+            <td>
+                749.9450
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                Switzerland
+            </td>
+            <td>
+                664.2349
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                Germany
+            </td>
+            <td>
+                635.0223
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                China (Mainland)
+            </td>
+            <td>
+                578.7473
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                Hong Kong SAR
+            </td>
+            <td>
+                513.1582
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                France
+            </td>
+            <td>
+                385.9691
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                Sweden
+            </td>
+            <td>
+                382.8463
+            </td>
+        </tr>
+        <tr>
+            <th>
+                11
+            </th>
+            <td>
+                New Zealand
+            </td>
+            <td>
+                344.3393
+            </td>
+        </tr>
+        <tr>
+            <th>
+                12
+            </th>
+            <td>
+                Belgium
+            </td>
+            <td>
+                300.6716
+            </td>
+        </tr>
+        <tr>
+            <th>
+                13
+            </th>
+            <td>
+                Denmark
+            </td>
+            <td>
+                217.8851
+            </td>
+        </tr>
+        <tr>
+            <th>
+                14
+            </th>
+            <td>
+                Finland
+            </td>
+            <td>
+                210.7134
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q14</h1>
+<table border="1" class="dataframe" data-question="q14">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Country
+            </th>
+            <th>
+                Average Citations per International
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                Singapore
+            </td>
+            <td>
+                92.950000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                Australia
+            </td>
+            <td>
+                82.001726
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                Hong Kong SAR
+            </td>
+            <td>
+                78.318000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Switzerland
+            </td>
+            <td>
+                78.004875
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                Netherlands
+            </td>
+            <td>
+                58.039117
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                United Kingdom
+            </td>
+            <td>
+                56.838479
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                Sweden
+            </td>
+            <td>
+                52.991567
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                Canada
+            </td>
+            <td>
+                48.342191
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                Denmark
+            </td>
+            <td>
+                47.686267
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                Belgium
+            </td>
+            <td>
+                47.580433
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                Norway
+            </td>
+            <td>
+                44.820000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                11
+            </th>
+            <td>
+                Saudi Arabia
+            </td>
+            <td>
+                43.294600
+            </td>
+        </tr>
+        <tr>
+            <th>
+                12
+            </th>
+            <td>
+                Finland
+            </td>
+            <td>
+                42.823267
+            </td>
+        </tr>
+        <tr>
+            <th>
+                13
+            </th>
+            <td>
+                Ireland
+            </td>
+            <td>
+                38.282033
+            </td>
+        </tr>
+        <tr>
+            <th>
+                14
+            </th>
+            <td>
+                New Zealand
+            </td>
+            <td>
+                38.215660
+            </td>
+        </tr>
+        <tr>
+            <th>
+                15
+            </th>
+            <td>
+                Qatar
+            </td>
+            <td>
+                36.900000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                16
+            </th>
+            <td>
+                China (Mainland)
+            </td>
+            <td>
+                36.544577
+            </td>
+        </tr>
+        <tr>
+            <th>
+                17
+            </th>
+            <td>
+                United States
+            </td>
+            <td>
+                35.593264
+            </td>
+        </tr>
+        <tr>
+            <th>
+                18
+            </th>
+            <td>
+                France
+            </td>
+            <td>
+                32.660511
+            </td>
+        </tr>
+        <tr>
+            <th>
+                19
+            </th>
+            <td>
+                United Arab Emirates
+            </td>
+            <td>
+                31.700000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                20
+            </th>
+            <td>
+                Austria
+            </td>
+            <td>
+                31.611433
+            </td>
+        </tr>
+        <tr>
+            <th>
+                21
+            </th>
+            <td>
+                South Africa
+            </td>
+            <td>
+                27.696600
+            </td>
+        </tr>
+        <tr>
+            <th>
+                22
+            </th>
+            <td>
+                Israel
+            </td>
+            <td>
+                26.089950
+            </td>
+        </tr>
+        <tr>
+            <th>
+                23
+            </th>
+            <td>
+                Germany
+            </td>
+            <td>
+                24.701627
+            </td>
+        </tr>
+        <tr>
+            <th>
+                24
+            </th>
+            <td>
+                Lebanon
+            </td>
+            <td>
+                14.980000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                25
+            </th>
+            <td>
+                South Korea
+            </td>
+            <td>
+                14.784700
+            </td>
+        </tr>
+        <tr>
+            <th>
+                26
+            </th>
+            <td>
+                Spain
+            </td>
+            <td>
+                14.626767
+            </td>
+        </tr>
+        <tr>
+            <th>
+                27
+            </th>
+            <td>
+                Italy
+            </td>
+            <td>
+                10.578600
+            </td>
+        </tr>
+        <tr>
+            <th>
+                28
+            </th>
+            <td>
+                Taiwan
+            </td>
+            <td>
+                8.763275
+            </td>
+        </tr>
+        <tr>
+            <th>
+                29
+            </th>
+            <td>
+                Brunei
+            </td>
+            <td>
+                8.500000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                30
+            </th>
+            <td>
+                Japan
+            </td>
+            <td>
+                7.592830
+            </td>
+        </tr>
+        <tr>
+            <th>
+                31
+            </th>
+            <td>
+                Malaysia
+            </td>
+            <td>
+                5.926371
+            </td>
+        </tr>
+        <tr>
+            <th>
+                32
+            </th>
+            <td>
+                Estonia
+            </td>
+            <td>
+                5.720000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                33
+            </th>
+            <td>
+                Portugal
+            </td>
+            <td>
+                5.283600
+            </td>
+        </tr>
+        <tr>
+            <th>
+                34
+            </th>
+            <td>
+                India
+            </td>
+            <td>
+                3.698383
+            </td>
+        </tr>
+        <tr>
+            <th>
+                35
+            </th>
+            <td>
+                Brazil
+            </td>
+            <td>
+                3.292400
+            </td>
+        </tr>
+        <tr>
+            <th>
+                36
+            </th>
+            <td>
+                Czech Republic
+            </td>
+            <td>
+                2.557300
+            </td>
+        </tr>
+        <tr>
+            <th>
+                37
+            </th>
+            <td>
+                Chile
+            </td>
+            <td>
+                1.772050
+            </td>
+        </tr>
+        <tr>
+            <th>
+                38
+            </th>
+            <td>
+                Russia
+            </td>
+            <td>
+                1.433386
+            </td>
+        </tr>
+        <tr>
+            <th>
+                39
+            </th>
+            <td>
+                Indonesia
+            </td>
+            <td>
+                1.180100
+            </td>
+        </tr>
+        <tr>
+            <th>
+                40
+            </th>
+            <td>
+                Argentina
+            </td>
+            <td>
+                1.146600
+            </td>
+        </tr>
+        <tr>
+            <th>
+                41
+            </th>
+            <td>
+                Colombia
+            </td>
+            <td>
+                1.059850
+            </td>
+        </tr>
+        <tr>
+            <th>
+                42
+            </th>
+            <td>
+                Mexico
+            </td>
+            <td>
+                0.716750
+            </td>
+        </tr>
+        <tr>
+            <th>
+                43
+            </th>
+            <td>
+                Kazakhstan
+            </td>
+            <td>
+                0.539850
+            </td>
+        </tr>
+        <tr>
+            <th>
+                44
+            </th>
+            <td>
+                Poland
+            </td>
+            <td>
+                0.470300
+            </td>
+        </tr>
+        <tr>
+            <th>
+                45
+            </th>
+            <td>
+                Thailand
+            </td>
+            <td>
+                0.445800
+            </td>
+        </tr>
+        <tr>
+            <th>
+                46
+            </th>
+            <td>
+                Belarus
+            </td>
+            <td>
+                0.044200
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q15</h1>
+<table border="1" class="dataframe" data-question="q15">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Country
+            </th>
+            <th>
+                Institution Name
+            </th>
+            <th>
+                Maximum Citations per International
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                United States
+            </td>
+            <td>
+                Massachusetts Institute of Technology (MIT)
+            </td>
+            <td>
+                100.0000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                Hong Kong SAR
+            </td>
+            <td>
+                City University of Hong Kong
+            </td>
+            <td>
+                99.9000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                Switzerland
+            </td>
+            <td>
+                University of Bern
+            </td>
+            <td>
+                99.2000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Australia
+            </td>
+            <td>
+                The University of Western Australia
+            </td>
+            <td>
+                98.9000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                Canada
+            </td>
+            <td>
+                Western University
+            </td>
+            <td>
+                98.0051
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                Macau SAR
+            </td>
+            <td>
+                University of Macau
+            </td>
+            <td>
+                96.9000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                China (Mainland)
+            </td>
+            <td>
+                Zhejiang University
+            </td>
+            <td>
+                95.3552
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                Singapore
+            </td>
+            <td>
+                Nanyang Technological University, Singapore (NTU)
+            </td>
+            <td>
+                94.4000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                United Kingdom
+            </td>
+            <td>
+                Imperial College London
+            </td>
+            <td>
+                94.0000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                France
+            </td>
+            <td>
+                Institut Polytechnique de Paris
+            </td>
+            <td>
+                92.3930
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                Netherlands
+            </td>
+            <td>
+                University of Amsterdam
+            </td>
+            <td>
+                90.6444
+            </td>
+        </tr>
+        <tr>
+            <th>
+                11
+            </th>
+            <td>
+                Belgium
+            </td>
+            <td>
+                KU Leuven
+            </td>
+            <td>
+                86.3037
+            </td>
+        </tr>
+        <tr>
+            <th>
+                12
+            </th>
+            <td>
+                Sweden
+            </td>
+            <td>
+                KTH Royal Institute of Technology
+            </td>
+            <td>
+                84.0664
+            </td>
+        </tr>
+        <tr>
+            <th>
+                13
+            </th>
+            <td>
+                Denmark
+            </td>
+            <td>
+                Technical University of Denmark
+            </td>
+            <td>
+                83.4000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                14
+            </th>
+            <td>
+                Finland
+            </td>
+            <td>
+                Aalto University
+            </td>
+            <td>
+                75.8270
+            </td>
+        </tr>
+        <tr>
+            <th>
+                15
+            </th>
+            <td>
+                New Zealand
+            </td>
+            <td>
+                University of Waikato
+            </td>
+            <td>
+                74.4750
+            </td>
+        </tr>
+        <tr>
+            <th>
+                16
+            </th>
+            <td>
+                Cyprus
+            </td>
+            <td>
+                University of Cyprus (UCY)
+            </td>
+            <td>
+                72.2762
+            </td>
+        </tr>
+        <tr>
+            <th>
+                17
+            </th>
+            <td>
+                Austria
+            </td>
+            <td>
+                Technische Universität Wien
+            </td>
+            <td>
+                67.9601
+            </td>
+        </tr>
+        <tr>
+            <th>
+                18
+            </th>
+            <td>
+                Saudi Arabia
+            </td>
+            <td>
+                King Fahd University of Petroleum & Minerals
+            </td>
+            <td>
+                65.8000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                19
+            </th>
+            <td>
+                Ireland
+            </td>
+            <td>
+                Trinity College Dublin, The University of Dublin
+            </td>
+            <td>
+                63.9718
+            </td>
+        </tr>
+        <tr>
+            <th>
+                20
+            </th>
+            <td>
+                Malaysia
+            </td>
+            <td>
+                Universiti Teknologi PETRONAS (UTP)
+            </td>
+            <td>
+                55.9764
+            </td>
+        </tr>
+        <tr>
+            <th>
+                21
+            </th>
+            <td>
+                Norway
+            </td>
+            <td>
+                University of Oslo
+            </td>
+            <td>
+                55.2762
+            </td>
+        </tr>
+        <tr>
+            <th>
+                22
+            </th>
+            <td>
+                Germany
+            </td>
+            <td>
+                KIT, Karlsruhe Institute of Technology
+            </td>
+            <td>
+                50.6176
+            </td>
+        </tr>
+        <tr>
+            <th>
+                23
+            </th>
+            <td>
+                United Arab Emirates
+            </td>
+            <td>
+                Khalifa University of Science and Technology
+            </td>
+            <td>
+                48.3000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                24
+            </th>
+            <td>
+                Spain
+            </td>
+            <td>
+                Universitat Pompeu Fabra (Barcelona)
+            </td>
+            <td>
+                43.3124
+            </td>
+        </tr>
+        <tr>
+            <th>
+                25
+            </th>
+            <td>
+                South Korea
+            </td>
+            <td>
+                Ulsan National Institute of Science and Technology (UNIST)
+            </td>
+            <td>
+                40.9190
+            </td>
+        </tr>
+        <tr>
+            <th>
+                26
+            </th>
+            <td>
+                Israel
+            </td>
+            <td>
+                Tel Aviv University
+            </td>
+            <td>
+                40.3596
+            </td>
+        </tr>
+        <tr>
+            <th>
+                27
+            </th>
+            <td>
+                Qatar
+            </td>
+            <td>
+                Qatar University
+            </td>
+            <td>
+                38.8000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                28
+            </th>
+            <td>
+                Italy
+            </td>
+            <td>
+                Politecnico di Milano
+            </td>
+            <td>
+                30.8154
+            </td>
+        </tr>
+        <tr>
+            <th>
+                29
+            </th>
+            <td>
+                Luxembourg
+            </td>
+            <td>
+                University of Luxembourg
+            </td>
+            <td>
+                28.6000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                30
+            </th>
+            <td>
+                South Africa
+            </td>
+            <td>
+                University of Cape Town
+            </td>
+            <td>
+                27.3743
+            </td>
+        </tr>
+        <tr>
+            <th>
+                31
+            </th>
+            <td>
+                Oman
+            </td>
+            <td>
+                Sultan Qaboos University
+            </td>
+            <td>
+                18.4000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                32
+            </th>
+            <td>
+                Japan
+            </td>
+            <td>
+                Osaka University
+            </td>
+            <td>
+                17.9322
+            </td>
+        </tr>
+        <tr>
+            <th>
+                33
+            </th>
+            <td>
+                Lebanon
+            </td>
+            <td>
+                American University of Beirut (AUB)
+            </td>
+            <td>
+                16.4076
+            </td>
+        </tr>
+        <tr>
+            <th>
+                34
+            </th>
+            <td>
+                Iran, Islamic Republic of
+            </td>
+            <td>
+                Amirkabir University of Technology
+            </td>
+            <td>
+                15.8682
+            </td>
+        </tr>
+        <tr>
+            <th>
+                35
+            </th>
+            <td>
+                Taiwan
+            </td>
+            <td>
+                National Yang Ming Chiao Tung University
+            </td>
+            <td>
+                9.8990
+            </td>
+        </tr>
+        <tr>
+            <th>
+                36
+            </th>
+            <td>
+                Brunei
+            </td>
+            <td>
+                Universiti Brunei Darussalam (UBD)
+            </td>
+            <td>
+                8.2000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                37
+            </th>
+            <td>
+                Turkey
+            </td>
+            <td>
+                Koç University
+            </td>
+            <td>
+                7.8078
+            </td>
+        </tr>
+        <tr>
+            <th>
+                38
+            </th>
+            <td>
+                Russia
+            </td>
+            <td>
+                National Research Nuclear University MEPhI (Moscow Engineering Physics Institute)
+            </td>
+            <td>
+                5.8969
+            </td>
+        </tr>
+        <tr>
+            <th>
+                39
+            </th>
+            <td>
+                Portugal
+            </td>
+            <td>
+                Universidade Nova de Lisboa
+            </td>
+            <td>
+                5.8080
+            </td>
+        </tr>
+        <tr>
+            <th>
+                40
+            </th>
+            <td>
+                Estonia
+            </td>
+            <td>
+                University of Tartu
+            </td>
+            <td>
+                5.3475
+            </td>
+        </tr>
+        <tr>
+            <th>
+                41
+            </th>
+            <td>
+                India
+            </td>
+            <td>
+                Indian Institute of Science
+            </td>
+            <td>
+                4.8000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                42
+            </th>
+            <td>
+                Czech Republic
+            </td>
+            <td>
+                Masaryk University
+            </td>
+            <td>
+                4.7120
+            </td>
+        </tr>
+        <tr>
+            <th>
+                43
+            </th>
+            <td>
+                Egypt
+            </td>
+            <td>
+                The American University in Cairo
+            </td>
+            <td>
+                4.5034
+            </td>
+        </tr>
+        <tr>
+            <th>
+                44
+            </th>
+            <td>
+                Pakistan
+            </td>
+            <td>
+                Quaid-i-Azam University
+            </td>
+            <td>
+                3.2980
+            </td>
+        </tr>
+        <tr>
+            <th>
+                45
+            </th>
+            <td>
+                Brazil
+            </td>
+            <td>
+                Universidade Estadual de Campinas (Unicamp)
+            </td>
+            <td>
+                2.9133
+            </td>
+        </tr>
+        <tr>
+            <th>
+                46
+            </th>
+            <td>
+                Greece
+            </td>
+            <td>
+                National and Kapodistrian University of Athens
+            </td>
+            <td>
+                2.8809
+            </td>
+        </tr>
+        <tr>
+            <th>
+                47
+            </th>
+            <td>
+                Indonesia
+            </td>
+            <td>
+                Bandung Institute of Technology (ITB)
+            </td>
+            <td>
+                2.1100
+            </td>
+        </tr>
+        <tr>
+            <th>
+                48
+            </th>
+            <td>
+                Chile
+            </td>
+            <td>
+                Pontificia Universidad Católica de Chile (UC)
+            </td>
+            <td>
+                1.9024
+            </td>
+        </tr>
+        <tr>
+            <th>
+                49
+            </th>
+            <td>
+                Colombia
+            </td>
+            <td>
+                Universidad de los Andes
+            </td>
+            <td>
+                1.5576
+            </td>
+        </tr>
+        <tr>
+            <th>
+                50
+            </th>
+            <td>
+                Argentina
+            </td>
+            <td>
+                Universidad de Buenos Aires (UBA)
+            </td>
+            <td>
+                1.0642
+            </td>
+        </tr>
+        <tr>
+            <th>
+                51
+            </th>
+            <td>
+                Thailand
+            </td>
+            <td>
+                Chulalongkorn University
+            </td>
+            <td>
+                1.0500
+            </td>
+        </tr>
+        <tr>
+            <th>
+                52
+            </th>
+            <td>
+                Kazakhstan
+            </td>
+            <td>
+                L.N. Gumilyov Eurasian National University (ENU)
+            </td>
+            <td>
+                0.8302
+            </td>
+        </tr>
+        <tr>
+            <th>
+                53
+            </th>
+            <td>
+                Mexico
+            </td>
+            <td>
+                Tecnológico de Monterrey
+            </td>
+            <td>
+                0.7521
+            </td>
+        </tr>
+        <tr>
+            <th>
+                54
+            </th>
+            <td>
+                Poland
+            </td>
+            <td>
+                University of Warsaw
+            </td>
+            <td>
+                0.6174
+            </td>
+        </tr>
+        <tr>
+            <th>
+                55
+            </th>
+            <td>
+                Lithuania
+            </td>
+            <td>
+                Vilnius University
+            </td>
+            <td>
+                0.3900
+            </td>
+        </tr>
+        <tr>
+            <th>
+                56
+            </th>
+            <td>
+                Peru
+            </td>
+            <td>
+                Pontificia Universidad Católica del Perú
+            </td>
+            <td>
+                0.0702
+            </td>
+        </tr>
+        <tr>
+            <th>
+                57
+            </th>
+            <td>
+                Belarus
+            </td>
+            <td>
+                Belarusian State University
+            </td>
+            <td>
+                0.0533
+            </td>
+        </tr>
+        <tr>
+            <th>
+                58
+            </th>
+            <td>
+                Philippines
+            </td>
+            <td>
+                University of the Philippines
+            </td>
+            <td>
+                0.0304
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q16</h1>
+<table border="1" class="dataframe" data-question="q16">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Country
+            </th>
+            <th>
+                Average Citations per Faculty
+            </th>
+            <th>
+                Average International Faculty
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                Canada
+            </td>
+            <td>
+                57.033333
+            </td>
+            <td>
+                93.3000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                Germany
+            </td>
+            <td>
+                27.200000
+            </td>
+            <td>
+                56.9000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                United Kingdom
+            </td>
+            <td>
+                77.937500
+            </td>
+            <td>
+                98.9375
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Hong Kong SAR
+            </td>
+            <td>
+                81.166667
+            </td>
+            <td>
+                100.0000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                United States
+            </td>
+            <td>
+                83.700000
+            </td>
+            <td>
+                65.9000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                Australia
+            </td>
+            <td>
+                91.180000
+            </td>
+            <td>
+                99.4200
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                Singapore
+            </td>
+            <td>
+                93.050000
+            </td>
+            <td>
+                100.0000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                Switzerland
+            </td>
+            <td>
+                99.800000
+            </td>
+            <td>
+                100.0000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                France
+            </td>
+            <td>
+                73.350000
+            </td>
+            <td>
+                75.9000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                Japan
+            </td>
+            <td>
+                69.650000
+            </td>
+            <td>
+                4.3500
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                China (Mainland)
+            </td>
+            <td>
+                84.240000
+            </td>
+            <td>
+                55.5400
+            </td>
+        </tr>
+        <tr>
+            <th>
+                11
+            </th>
+            <td>
+                South Korea
+            </td>
+            <td>
+                86.900000
+            </td>
+            <td>
+                6.2500
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q17</h1>
+<table border="1" class="dataframe" data-question="q17">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Rank
+            </th>
+            <th>
+                Overall
+            </th>
+            <th>
+                fit
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                1
+            </td>
+            <td>
+                100.0
+            </td>
+            <td>
+                -142.320024
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                2
+            </td>
+            <td>
+                99.5
+            </td>
+            <td>
+                -138.773723
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                3
+            </td>
+            <td>
+                98.7
+            </td>
+            <td>
+                -133.099642
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                3
+            </td>
+            <td>
+                98.7
+            </td>
+            <td>
+                -133.099642
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                5
+            </td>
+            <td>
+                98.0
+            </td>
+            <td>
+                -128.134821
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                6
+            </td>
+            <td>
+                97.4
+            </td>
+            <td>
+                -123.879260
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                7
+            </td>
+            <td>
+                97.3
+            </td>
+            <td>
+                -123.170000
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                8
+            </td>
+            <td>
+                95.4
+            </td>
+            <td>
+                -109.694057
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                8
+            </td>
+            <td>
+                95.4
+            </td>
+            <td>
+                -109.694057
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                10
+            </td>
+            <td>
+                94.5
+            </td>
+            <td>
+                -103.310716
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                11
+            </td>
+            <td>
+                93.9
+            </td>
+            <td>
+                -99.055155
+            </td>
+        </tr>
+        <tr>
+            <th>
+                11
+            </th>
+            <td>
+                12
+            </td>
+            <td>
+                90.8
+            </td>
+            <td>
+                -77.068090
+            </td>
+        </tr>
+        <tr>
+            <th>
+                12
+            </th>
+            <td>
+                13
+            </td>
+            <td>
+                90.7
+            </td>
+            <td>
+                -76.358830
+            </td>
+        </tr>
+        <tr>
+            <th>
+                13
+            </th>
+            <td>
+                14
+            </td>
+            <td>
+                90.2
+            </td>
+            <td>
+                -72.812529
+            </td>
+        </tr>
+        <tr>
+            <th>
+                14
+            </th>
+            <td>
+                14
+            </td>
+            <td>
+                90.2
+            </td>
+            <td>
+                -72.812529
+            </td>
+        </tr>
+        <tr>
+            <th>
+                15
+            </th>
+            <td>
+                16
+            </td>
+            <td>
+                89.9
+            </td>
+            <td>
+                -70.684749
+            </td>
+        </tr>
+        <tr>
+            <th>
+                16
+            </th>
+            <td>
+                17
+            </td>
+            <td>
+                89.0
+            </td>
+            <td>
+                -64.301408
+            </td>
+        </tr>
+        <tr>
+            <th>
+                17
+            </th>
+            <td>
+                18
+            </td>
+            <td>
+                88.8
+            </td>
+            <td>
+                -62.882887
+            </td>
+        </tr>
+        <tr>
+            <th>
+                18
+            </th>
+            <td>
+                19
+            </td>
+            <td>
+                88.7
+            </td>
+            <td>
+                -62.173627
+            </td>
+        </tr>
+        <tr>
+            <th>
+                19
+            </th>
+            <td>
+                20
+            </td>
+            <td>
+                88.6
+            </td>
+            <td>
+                -61.464367
+            </td>
+        </tr>
+        <tr>
+            <th>
+                20
+            </th>
+            <td>
+                21
+            </td>
+            <td>
+                88.3
+            </td>
+            <td>
+                -59.336587
+            </td>
+        </tr>
+        <tr>
+            <th>
+                21
+            </th>
+            <td>
+                22
+            </td>
+            <td>
+                86.3
+            </td>
+            <td>
+                -45.151384
+            </td>
+        </tr>
+        <tr>
+            <th>
+                22
+            </th>
+            <td>
+                23
+            </td>
+            <td>
+                86.2
+            </td>
+            <td>
+                -44.442124
+            </td>
+        </tr>
+        <tr>
+            <th>
+                23
+            </th>
+            <td>
+                23
+            </td>
+            <td>
+                86.2
+            </td>
+            <td>
+                -44.442124
+            </td>
+        </tr>
+        <tr>
+            <th>
+                24
+            </th>
+            <td>
+                25
+            </td>
+            <td>
+                85.9
+            </td>
+            <td>
+                -42.314343
+            </td>
+        </tr>
+        <tr>
+            <th>
+                25
+            </th>
+            <td>
+                26
+            </td>
+            <td>
+                85.3
+            </td>
+            <td>
+                -38.058782
+            </td>
+        </tr>
+        <tr>
+            <th>
+                26
+            </th>
+            <td>
+                27
+            </td>
+            <td>
+                84.0
+            </td>
+            <td>
+                -28.838400
+            </td>
+        </tr>
+        <tr>
+            <th>
+                27
+            </th>
+            <td>
+                27
+            </td>
+            <td>
+                84.0
+            </td>
+            <td>
+                -28.838400
+            </td>
+        </tr>
+        <tr>
+            <th>
+                28
+            </th>
+            <td>
+                27
+            </td>
+            <td>
+                84.0
+            </td>
+            <td>
+                -28.838400
+            </td>
+        </tr>
+        <tr>
+            <th>
+                29
+            </th>
+            <td>
+                30
+            </td>
+            <td>
+                82.8
+            </td>
+            <td>
+                -20.327278
+            </td>
+        </tr>
+        <tr>
+            <th>
+                30
+            </th>
+            <td>
+                31
+            </td>
+            <td>
+                82.6
+            </td>
+            <td>
+                -18.908758
+            </td>
+        </tr>
+        <tr>
+            <th>
+                31
+            </th>
+            <td>
+                32
+            </td>
+            <td>
+                82.5
+            </td>
+            <td>
+                -18.199498
+            </td>
+        </tr>
+        <tr>
+            <th>
+                32
+            </th>
+            <td>
+                33
+            </td>
+            <td>
+                82.3
+            </td>
+            <td>
+                -16.780978
+            </td>
+        </tr>
+        <tr>
+            <th>
+                33
+            </th>
+            <td>
+                34
+            </td>
+            <td>
+                82.2
+            </td>
+            <td>
+                -16.071718
+            </td>
+        </tr>
+        <tr>
+            <th>
+                34
+            </th>
+            <td>
+                35
+            </td>
+            <td>
+                82.0
+            </td>
+            <td>
+                -14.653197
+            </td>
+        </tr>
+        <tr>
+            <th>
+                35
+            </th>
+            <td>
+                36
+            </td>
+            <td>
+                81.7
+            </td>
+            <td>
+                -12.525417
+            </td>
+        </tr>
+        <tr>
+            <th>
+                36
+            </th>
+            <td>
+                37
+            </td>
+            <td>
+                81.4
+            </td>
+            <td>
+                -10.397636
+            </td>
+        </tr>
+        <tr>
+            <th>
+                37
+            </th>
+            <td>
+                38
+            </td>
+            <td>
+                80.4
+            </td>
+            <td>
+                -3.305035
+            </td>
+        </tr>
+        <tr>
+            <th>
+                38
+            </th>
+            <td>
+                39
+            </td>
+            <td>
+                80.3
+            </td>
+            <td>
+                -2.595775
+            </td>
+        </tr>
+        <tr>
+            <th>
+                39
+            </th>
+            <td>
+                40
+            </td>
+            <td>
+                79.7
+            </td>
+            <td>
+                1.659786
+            </td>
+        </tr>
+        <tr>
+            <th>
+                40
+            </th>
+            <td>
+                41
+            </td>
+            <td>
+                79.1
+            </td>
+            <td>
+                5.915347
+            </td>
+        </tr>
+        <tr>
+            <th>
+                41
+            </th>
+            <td>
+                42
+            </td>
+            <td>
+                78.9
+            </td>
+            <td>
+                7.333867
+            </td>
+        </tr>
+        <tr>
+            <th>
+                42
+            </th>
+            <td>
+                43
+            </td>
+            <td>
+                77.7
+            </td>
+            <td>
+                15.844989
+            </td>
+        </tr>
+        <tr>
+            <th>
+                43
+            </th>
+            <td>
+                44
+            </td>
+            <td>
+                77.6
+            </td>
+            <td>
+                16.554249
+            </td>
+        </tr>
+        <tr>
+            <th>
+                44
+            </th>
+            <td>
+                45
+            </td>
+            <td>
+                77.4
+            </td>
+            <td>
+                17.972770
+            </td>
+        </tr>
+        <tr>
+            <th>
+                45
+            </th>
+            <td>
+                46
+            </td>
+            <td>
+                77.1
+            </td>
+            <td>
+                20.100550
+            </td>
+        </tr>
+        <tr>
+            <th>
+                46
+            </th>
+            <td>
+                47
+            </td>
+            <td>
+                76.6
+            </td>
+            <td>
+                23.646851
+            </td>
+        </tr>
+        <tr>
+            <th>
+                47
+            </th>
+            <td>
+                48
+            </td>
+            <td>
+                76.1
+            </td>
+            <td>
+                27.193151
+            </td>
+        </tr>
+        <tr>
+            <th>
+                48
+            </th>
+            <td>
+                49
+            </td>
+            <td>
+                75.9
+            </td>
+            <td>
+                28.611672
+            </td>
+        </tr>
+        <tr>
+            <th>
+                49
+            </th>
+            <td>
+                49
+            </td>
+            <td>
+                75.8
+            </td>
+            <td>
+                29.320932
+            </td>
+        </tr>
+        <tr>
+            <th>
+                50
+            </th>
+            <td>
+                50
+            </td>
+            <td>
+                75.6
+            </td>
+            <td>
+                30.739452
+            </td>
+        </tr>
+        <tr>
+            <th>
+                51
+            </th>
+            <td>
+                50
+            </td>
+            <td>
+                75.6
+            </td>
+            <td>
+                30.739452
+            </td>
+        </tr>
+        <tr>
+            <th>
+                52
+            </th>
+            <td>
+                52
+            </td>
+            <td>
+                75.2
+            </td>
+            <td>
+                33.576493
+            </td>
+        </tr>
+        <tr>
+            <th>
+                53
+            </th>
+            <td>
+                53
+            </td>
+            <td>
+                74.6
+            </td>
+            <td>
+                37.832054
+            </td>
+        </tr>
+        <tr>
+            <th>
+                54
+            </th>
+            <td>
+                53
+            </td>
+            <td>
+                74.6
+            </td>
+            <td>
+                37.832054
+            </td>
+        </tr>
+        <tr>
+            <th>
+                55
+            </th>
+            <td>
+                55
+            </td>
+            <td>
+                73.8
+            </td>
+            <td>
+                43.506135
+            </td>
+        </tr>
+        <tr>
+            <th>
+                56
+            </th>
+            <td>
+                56
+            </td>
+            <td>
+                73.5
+            </td>
+            <td>
+                45.633915
+            </td>
+        </tr>
+        <tr>
+            <th>
+                57
+            </th>
+            <td>
+                57
+            </td>
+            <td>
+                73.1
+            </td>
+            <td>
+                48.470956
+            </td>
+        </tr>
+        <tr>
+            <th>
+                58
+            </th>
+            <td>
+                58
+            </td>
+            <td>
+                72.2
+            </td>
+            <td>
+                54.854297
+            </td>
+        </tr>
+        <tr>
+            <th>
+                59
+            </th>
+            <td>
+                60
+            </td>
+            <td>
+                71.3
+            </td>
+            <td>
+                61.237639
+            </td>
+        </tr>
+        <tr>
+            <th>
+                60
+            </th>
+            <td>
+                61
+            </td>
+            <td>
+                71.2
+            </td>
+            <td>
+                61.946899
+            </td>
+        </tr>
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+            </td>
+        </tr>
+        <tr>
+            <th>
+                489
+            </th>
+            <td>
+                490
+            </td>
+            <td>
+                24.3
+            </td>
+            <td>
+                394.589908
+            </td>
+        </tr>
+        <tr>
+            <th>
+                490
+            </th>
+            <td>
+                490
+            </td>
+            <td>
+                24.3
+            </td>
+            <td>
+                394.589908
+            </td>
+        </tr>
+        <tr>
+            <th>
+                491
+            </th>
+            <td>
+                492
+            </td>
+            <td>
+                24.2
+            </td>
+            <td>
+                395.299168
+            </td>
+        </tr>
+        <tr>
+            <th>
+                492
+            </th>
+            <td>
+                492
+            </td>
+            <td>
+                24.2
+            </td>
+            <td>
+                395.299168
+            </td>
+        </tr>
+        <tr>
+            <th>
+                493
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                24.1
+            </td>
+            <td>
+                396.008429
+            </td>
+        </tr>
+        <tr>
+            <th>
+                494
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                24.1
+            </td>
+            <td>
+                396.008429
+            </td>
+        </tr>
+        <tr>
+            <th>
+                495
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                24.1
+            </td>
+            <td>
+                396.008429
+            </td>
+        </tr>
+        <tr>
+            <th>
+                496
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                24.1
+            </td>
+            <td>
+                396.008429
+            </td>
+        </tr>
+        <tr>
+            <th>
+                497
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                24.1
+            </td>
+            <td>
+                396.008429
+            </td>
+        </tr>
+        <tr>
+            <th>
+                498
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                24.1
+            </td>
+            <td>
+                396.008429
+            </td>
+        </tr>
+        <tr>
+            <th>
+                499
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                24.1
+            </td>
+            <td>
+                396.008429
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q18</h1>
+<table border="1" class="dataframe" data-question="q18">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Rank
+            </th>
+            <th>
+                Inverse Overall
+            </th>
+            <th>
+                fit
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                1
+            </td>
+            <td>
+                0.010000
+            </td>
+            <td>
+                -3.168433
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                2
+            </td>
+            <td>
+                0.010050
+            </td>
+            <td>
+                -2.371054
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                3
+            </td>
+            <td>
+                0.010132
+            </td>
+            <td>
+                -1.078443
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                3
+            </td>
+            <td>
+                0.010132
+            </td>
+            <td>
+                -1.078443
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                5
+            </td>
+            <td>
+                0.010204
+            </td>
+            <td>
+                0.069903
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                6
+            </td>
+            <td>
+                0.010267
+            </td>
+            <td>
+                1.067337
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                7
+            </td>
+            <td>
+                0.010277
+            </td>
+            <td>
+                1.234772
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                8
+            </td>
+            <td>
+                0.010482
+            </td>
+            <td>
+                4.482730
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                8
+            </td>
+            <td>
+                0.010482
+            </td>
+            <td>
+                4.482730
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                10
+            </td>
+            <td>
+                0.010582
+            </td>
+            <td>
+                6.066822
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                11
+            </td>
+            <td>
+                0.010650
+            </td>
+            <td>
+                7.139754
+            </td>
+        </tr>
+        <tr>
+            <th>
+                11
+            </th>
+            <td>
+                12
+            </td>
+            <td>
+                0.011013
+            </td>
+            <td>
+                12.909122
+            </td>
+        </tr>
+        <tr>
+            <th>
+                12
+            </th>
+            <td>
+                13
+            </td>
+            <td>
+                0.011025
+            </td>
+            <td>
+                13.101797
+            </td>
+        </tr>
+        <tr>
+            <th>
+                13
+            </th>
+            <td>
+                14
+            </td>
+            <td>
+                0.011086
+            </td>
+            <td>
+                14.071579
+            </td>
+        </tr>
+        <tr>
+            <th>
+                14
+            </th>
+            <td>
+                14
+            </td>
+            <td>
+                0.011086
+            </td>
+            <td>
+                14.071579
+            </td>
+        </tr>
+        <tr>
+            <th>
+                15
+            </th>
+            <td>
+                16
+            </td>
+            <td>
+                0.011123
+            </td>
+            <td>
+                14.658627
+            </td>
+        </tr>
+        <tr>
+            <th>
+                16
+            </th>
+            <td>
+                17
+            </td>
+            <td>
+                0.011236
+            </td>
+            <td>
+                16.443514
+            </td>
+        </tr>
+        <tr>
+            <th>
+                17
+            </th>
+            <td>
+                18
+            </td>
+            <td>
+                0.011261
+            </td>
+            <td>
+                16.845069
+            </td>
+        </tr>
+        <tr>
+            <th>
+                18
+            </th>
+            <td>
+                19
+            </td>
+            <td>
+                0.011274
+            </td>
+            <td>
+                17.046526
+            </td>
+        </tr>
+        <tr>
+            <th>
+                19
+            </th>
+            <td>
+                20
+            </td>
+            <td>
+                0.011287
+            </td>
+            <td>
+                17.248437
+            </td>
+        </tr>
+        <tr>
+            <th>
+                20
+            </th>
+            <td>
+                21
+            </td>
+            <td>
+                0.011325
+            </td>
+            <td>
+                17.856915
+            </td>
+        </tr>
+        <tr>
+            <th>
+                21
+            </th>
+            <td>
+                22
+            </td>
+            <td>
+                0.011587
+            </td>
+            <td>
+                22.021546
+            </td>
+        </tr>
+        <tr>
+            <th>
+                22
+            </th>
+            <td>
+                23
+            </td>
+            <td>
+                0.011601
+            </td>
+            <td>
+                22.234850
+            </td>
+        </tr>
+        <tr>
+            <th>
+                23
+            </th>
+            <td>
+                23
+            </td>
+            <td>
+                0.011601
+            </td>
+            <td>
+                22.234850
+            </td>
+        </tr>
+        <tr>
+            <th>
+                24
+            </th>
+            <td>
+                25
+            </td>
+            <td>
+                0.011641
+            </td>
+            <td>
+                22.877744
+            </td>
+        </tr>
+        <tr>
+            <th>
+                25
+            </th>
+            <td>
+                26
+            </td>
+            <td>
+                0.011723
+            </td>
+            <td>
+                24.177097
+            </td>
+        </tr>
+        <tr>
+            <th>
+                26
+            </th>
+            <td>
+                27
+            </td>
+            <td>
+                0.011905
+            </td>
+            <td>
+                27.056040
+            </td>
+        </tr>
+        <tr>
+            <th>
+                27
+            </th>
+            <td>
+                27
+            </td>
+            <td>
+                0.011905
+            </td>
+            <td>
+                27.056040
+            </td>
+        </tr>
+        <tr>
+            <th>
+                28
+            </th>
+            <td>
+                27
+            </td>
+            <td>
+                0.011905
+            </td>
+            <td>
+                27.056040
+            </td>
+        </tr>
+        <tr>
+            <th>
+                29
+            </th>
+            <td>
+                30
+            </td>
+            <td>
+                0.012077
+            </td>
+            <td>
+                29.793764
+            </td>
+        </tr>
+        <tr>
+            <th>
+                30
+            </th>
+            <td>
+                31
+            </td>
+            <td>
+                0.012107
+            </td>
+            <td>
+                30.257785
+            </td>
+        </tr>
+        <tr>
+            <th>
+                31
+            </th>
+            <td>
+                32
+            </td>
+            <td>
+                0.012121
+            </td>
+            <td>
+                30.490639
+            </td>
+        </tr>
+        <tr>
+            <th>
+                32
+            </th>
+            <td>
+                33
+            </td>
+            <td>
+                0.012151
+            </td>
+            <td>
+                30.958045
+            </td>
+        </tr>
+        <tr>
+            <th>
+                33
+            </th>
+            <td>
+                34
+            </td>
+            <td>
+                0.012165
+            </td>
+            <td>
+                31.192601
+            </td>
+        </tr>
+        <tr>
+            <th>
+                34
+            </th>
+            <td>
+                35
+            </td>
+            <td>
+                0.012195
+            </td>
+            <td>
+                31.663429
+            </td>
+        </tr>
+        <tr>
+            <th>
+                35
+            </th>
+            <td>
+                36
+            </td>
+            <td>
+                0.012240
+            </td>
+            <td>
+                32.373993
+            </td>
+        </tr>
+        <tr>
+            <th>
+                36
+            </th>
+            <td>
+                37
+            </td>
+            <td>
+                0.012285
+            </td>
+            <td>
+                33.089795
+            </td>
+        </tr>
+        <tr>
+            <th>
+                37
+            </th>
+            <td>
+                38
+            </td>
+            <td>
+                0.012438
+            </td>
+            <td>
+                35.514381
+            </td>
+        </tr>
+        <tr>
+            <th>
+                38
+            </th>
+            <td>
+                39
+            </td>
+            <td>
+                0.012453
+            </td>
+            <td>
+                35.760161
+            </td>
+        </tr>
+        <tr>
+            <th>
+                39
+            </th>
+            <td>
+                40
+            </td>
+            <td>
+                0.012547
+            </td>
+            <td>
+                37.247793
+            </td>
+        </tr>
+        <tr>
+            <th>
+                40
+            </th>
+            <td>
+                41
+            </td>
+            <td>
+                0.012642
+            </td>
+            <td>
+                38.757993
+            </td>
+        </tr>
+        <tr>
+            <th>
+                41
+            </th>
+            <td>
+                42
+            </td>
+            <td>
+                0.012674
+            </td>
+            <td>
+                39.266497
+            </td>
+        </tr>
+        <tr>
+            <th>
+                42
+            </th>
+            <td>
+                43
+            </td>
+            <td>
+                0.012870
+            </td>
+            <td>
+                42.372495
+            </td>
+        </tr>
+        <tr>
+            <th>
+                43
+            </th>
+            <td>
+                44
+            </td>
+            <td>
+                0.012887
+            </td>
+            <td>
+                42.635665
+            </td>
+        </tr>
+        <tr>
+            <th>
+                44
+            </th>
+            <td>
+                45
+            </td>
+            <td>
+                0.012920
+            </td>
+            <td>
+                43.164044
+            </td>
+        </tr>
+        <tr>
+            <th>
+                45
+            </th>
+            <td>
+                46
+            </td>
+            <td>
+                0.012970
+            </td>
+            <td>
+                43.961752
+            </td>
+        </tr>
+        <tr>
+            <th>
+                46
+            </th>
+            <td>
+                47
+            </td>
+            <td>
+                0.013055
+            </td>
+            <td>
+                45.305150
+            </td>
+        </tr>
+        <tr>
+            <th>
+                47
+            </th>
+            <td>
+                48
+            </td>
+            <td>
+                0.013141
+            </td>
+            <td>
+                46.666202
+            </td>
+        </tr>
+        <tr>
+            <th>
+                48
+            </th>
+            <td>
+                49
+            </td>
+            <td>
+                0.013175
+            </td>
+            <td>
+                47.215644
+            </td>
+        </tr>
+        <tr>
+            <th>
+                49
+            </th>
+            <td>
+                49
+            </td>
+            <td>
+                0.013193
+            </td>
+            <td>
+                47.491452
+            </td>
+        </tr>
+        <tr>
+            <th>
+                50
+            </th>
+            <td>
+                50
+            </td>
+            <td>
+                0.013228
+            </td>
+            <td>
+                48.045257
+            </td>
+        </tr>
+        <tr>
+            <th>
+                51
+            </th>
+            <td>
+                50
+            </td>
+            <td>
+                0.013228
+            </td>
+            <td>
+                48.045257
+            </td>
+        </tr>
+        <tr>
+            <th>
+                52
+            </th>
+            <td>
+                52
+            </td>
+            <td>
+                0.013298
+            </td>
+            <td>
+                49.161705
+            </td>
+        </tr>
+        <tr>
+            <th>
+                53
+            </th>
+            <td>
+                53
+            </td>
+            <td>
+                0.013405
+            </td>
+            <td>
+                50.858825
+            </td>
+        </tr>
+        <tr>
+            <th>
+                54
+            </th>
+            <td>
+                53
+            </td>
+            <td>
+                0.013405
+            </td>
+            <td>
+                50.858825
+            </td>
+        </tr>
+        <tr>
+            <th>
+                55
+            </th>
+            <td>
+                55
+            </td>
+            <td>
+                0.013550
+            </td>
+            <td>
+                53.164578
+            </td>
+        </tr>
+        <tr>
+            <th>
+                56
+            </th>
+            <td>
+                56
+            </td>
+            <td>
+                0.013605
+            </td>
+            <td>
+                54.042176
+            </td>
+        </tr>
+        <tr>
+            <th>
+                57
+            </th>
+            <td>
+                57
+            </td>
+            <td>
+                0.013680
+            </td>
+            <td>
+                55.223512
+            </td>
+        </tr>
+        <tr>
+            <th>
+                58
+            </th>
+            <td>
+                58
+            </td>
+            <td>
+                0.013850
+            </td>
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+                57.929376
+            </td>
+        </tr>
+        <tr>
+            <th>
+                59
+            </th>
+            <td>
+                60
+            </td>
+            <td>
+                0.014025
+            </td>
+            <td>
+                60.703551
+            </td>
+        </tr>
+        <tr>
+            <th>
+                60
+            </th>
+            <td>
+                61
+            </td>
+            <td>
+                0.014045
+            </td>
+            <td>
+                61.016121
+            </td>
+        </tr>
+        <tr>
+            <th>
+                61
+            </th>
+            <td>
+                62
+            </td>
+            <td>
+                0.014085
+            </td>
+            <td>
+                61.643905
+            </td>
+        </tr>
+        <tr>
+            <th>
+                62
+            </th>
+            <td>
+                63
+            </td>
+            <td>
+                0.014124
+            </td>
+            <td>
+                62.275235
+            </td>
+        </tr>
+        <tr>
+            <th>
+                63
+            </th>
+            <td>
+                64
+            </td>
+            <td>
+                0.014265
+            </td>
+            <td>
+                64.513259
+            </td>
+        </tr>
+        <tr>
+            <th>
+                64
+            </th>
+            <td>
+                65
+            </td>
+            <td>
+                0.014327
+            </td>
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+                65.486154
+            </td>
+        </tr>
+        <tr>
+            <th>
+                65
+            </th>
+            <td>
+                66
+            </td>
+            <td>
+                0.014472
+            </td>
+            <td>
+                67.789094
+            </td>
+        </tr>
+        <tr>
+            <th>
+                66
+            </th>
+            <td>
+                67
+            </td>
+            <td>
+                0.014620
+            </td>
+            <td>
+                70.139170
+            </td>
+        </tr>
+        <tr>
+            <th>
+                67
+            </th>
+            <td>
+                68
+            </td>
+            <td>
+                0.014663
+            </td>
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+            </td>
+        </tr>
+        <tr>
+            <th>
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+            </td>
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+            </td>
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+                71.847462
+            </td>
+        </tr>
+        <tr>
+            <th>
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+            </th>
+            <td>
+                70
+            </td>
+            <td>
+                0.014881
+            </td>
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+            </td>
+        </tr>
+        <tr>
+            <th>
+                70
+            </th>
+            <td>
+                70
+            </td>
+            <td>
+                0.014881
+            </td>
+            <td>
+                74.281778
+            </td>
+        </tr>
+        <tr>
+            <th>
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+                493.849293
+            </td>
+        </tr>
+        <tr>
+            <th>
+                493
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                0.041494
+            </td>
+            <td>
+                496.570024
+            </td>
+        </tr>
+        <tr>
+            <th>
+                494
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                0.041494
+            </td>
+            <td>
+                496.570024
+            </td>
+        </tr>
+        <tr>
+            <th>
+                495
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                0.041494
+            </td>
+            <td>
+                496.570024
+            </td>
+        </tr>
+        <tr>
+            <th>
+                496
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                0.041494
+            </td>
+            <td>
+                496.570024
+            </td>
+        </tr>
+        <tr>
+            <th>
+                497
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                0.041494
+            </td>
+            <td>
+                496.570024
+            </td>
+        </tr>
+        <tr>
+            <th>
+                498
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                0.041494
+            </td>
+            <td>
+                496.570024
+            </td>
+        </tr>
+        <tr>
+            <th>
+                499
+            </th>
+            <td>
+                494
+            </td>
+            <td>
+                0.041494
+            </td>
+            <td>
+                496.570024
+            </td>
+        </tr>
+    </tbody>
+</table>
+<h1>Data Frame q20</h1>
+<table border="1" class="dataframe" data-question="q20">
+    <thead>
+        <tr style="text-align: right;">
+            <th>
+            </th>
+            <th>
+                Country
+            </th>
+            <th>
+                Number of Institutions
+            </th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <th>
+                0
+            </th>
+            <td>
+                United States
+            </td>
+            <td>
+                87
+            </td>
+        </tr>
+        <tr>
+            <th>
+                1
+            </th>
+            <td>
+                United Kingdom
+            </td>
+            <td>
+                49
+            </td>
+        </tr>
+        <tr>
+            <th>
+                2
+            </th>
+            <td>
+                Germany
+            </td>
+            <td>
+                31
+            </td>
+        </tr>
+        <tr>
+            <th>
+                3
+            </th>
+            <td>
+                Australia
+            </td>
+            <td>
+                26
+            </td>
+        </tr>
+        <tr>
+            <th>
+                4
+            </th>
+            <td>
+                China (Mainland)
+            </td>
+            <td>
+                26
+            </td>
+        </tr>
+        <tr>
+            <th>
+                5
+            </th>
+            <td>
+                Russia
+            </td>
+            <td>
+                17
+            </td>
+        </tr>
+        <tr>
+            <th>
+                6
+            </th>
+            <td>
+                Canada
+            </td>
+            <td>
+                16
+            </td>
+        </tr>
+        <tr>
+            <th>
+                7
+            </th>
+            <td>
+                Japan
+            </td>
+            <td>
+                16
+            </td>
+        </tr>
+        <tr>
+            <th>
+                8
+            </th>
+            <td>
+                South Korea
+            </td>
+            <td>
+                16
+            </td>
+        </tr>
+        <tr>
+            <th>
+                9
+            </th>
+            <td>
+                Italy
+            </td>
+            <td>
+                14
+            </td>
+        </tr>
+        <tr>
+            <th>
+                10
+            </th>
+            <td>
+                Other
+            </td>
+            <td>
+                202
+            </td>
+        </tr>
+    </tbody>
+</table>
diff --git a/p13/images/README.md b/p13/images/README.md
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--- /dev/null
+++ b/p13/images/README.md
@@ -0,0 +1,3 @@
+# Images
+
+Images from p13 are stored here.
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diff --git a/p13/images/summary.png b/p13/images/summary.png
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diff --git a/p13/p13.ipynb b/p13/p13.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..52f966f29eca996989c16ad6fdd8373e84ad4fc9
--- /dev/null
+++ b/p13/p13.ipynb
@@ -0,0 +1,3464 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a1203a9",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "# import and initialize otter\n",
+    "import otter\n",
+    "grader = otter.Notebook(\"p13.ipynb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71e8e0c0",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:50.836262Z",
+     "iopub.status.busy": "2023-12-06T02:03:50.836262Z",
+     "iopub.status.idle": "2023-12-06T02:03:54.572912Z",
+     "shell.execute_reply": "2023-12-06T02:03:54.570890Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import public_tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be5da508",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:54.578905Z",
+     "iopub.status.busy": "2023-12-06T02:03:54.577910Z",
+     "iopub.status.idle": "2023-12-06T02:03:54.583398Z",
+     "shell.execute_reply": "2023-12-06T02:03:54.582387Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# PLEASE FILL IN THE DETAILS\n",
+    "# enter none if you don't have a project partner\n",
+    "# you will have to add your partner as a group member on Gradescope even after you fill this\n",
+    "\n",
+    "# project: p13\n",
+    "# submitter: NETID1\n",
+    "# partner: NETID2  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ebcf32f",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " # Project 13: World University Rankings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "107475bf",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Learning Objectives:\n",
+    "\n",
+    "In this project, you will demonstrate how to:\n",
+    "\n",
+    "* query a database using SQL,\n",
+    "* process data using `pandas` **DataFrames**,\n",
+    "* create different types of plots.\n",
+    "\n",
+    "Please go through [Lab-P13](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/tree/main/lab-p13) before working on this project. The lab introduces some useful techniques related to this project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b83f32c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "<h2 style=\"color:red\">Warning (Note on Academic Misconduct):</h2>\n",
+    "\n",
+    "**IMPORTANT**: **P12 and P13 are two parts of the same data analysis.** You **cannot** switch project partners between these two projects. That is if you partnered up with someone for P12, you have to sustain that partnership until the end of P13.\n",
+    "\n",
+    "**You are  not allowed to use any late days for P13, even if you have late days remaining in your late days bank.** Now may be a good time to review [our course policies](https://cs220.cs.wisc.edu/f23/syllabus.html).\n",
+    "\n",
+    "Under any circumstances, **no more than two students are allowed to work together on a project** as mentioned in the course policies. If your code is flagged by our code similarity detection tools, **both partners will be responsible** for sharing/copying the code, even if the code is shared/copied by one of the partners with/from other non-partner student(s). Note that each case of plagiarism will be reported to the Dean of Students with a zero grade on the project. **If you think that someone cannot be your project partner then don’t make that student your lab partner.**\n",
+    "\n",
+    "**<font color = \"red\">Project partners must submit only one copy of their project on Gradescope, but they must include the names of both partners.</font>**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d37ea1eb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Testing your code:\n",
+    "\n",
+    "Along with this notebook, you must have downloaded the files `public_tests.py` and `expected_dfs.html`. If you are curious about how we test your code, you can explore this file, and specifically the output of the function `get_expected_json`, to understand the expected answers to the questions.\n",
+    "\n",
+    "For answers involving DataFrames, `public_tests.py` compares your tables to those in `expected_dfs.html`, so take a moment to open that file on a web browser (from Finder/Explorer). `public_tests.py` doesn't care if you have extra rows or columns, and it doesn't care about the order of the rows or columns. However, you must have the correct values at each index/column location shown in `expected_dfs.html`.\n",
+    "\n",
+    "**IMPORTANT Warning:** Do **not** download the dataset `rankings.json` **manually**. Use the `download` function from P12 to download it. When we run the autograder, this file `rankings.json` will **not** be in the directory. So, unless your `p13.ipynb` downloads these files, the Gradescope autograder will **deduct** points from your public score."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "463cc829",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Description:\n",
+    "\n",
+    "For your final CS220 project, you're going to continue analyzing world university rankings. However, we will be using a different dataset this time. The data for this project has been extracted from [here](https://www.topuniversities.com/university-rankings/world-university-rankings). Unlike the CWUR rankings we used in P12, the QS rankings dataset has various scores for the universities, and not just the rankings. This makes the QS rankings dataset more suitable for plotting (which you will be doing a lot of!).\n",
+    "\n",
+    "In this project, you'll have to dump your DataFrame to a SQLite database. You'll answer questions by doing queries on that database. Often, your answers will be in the form of a plot. Check these carefully, as the tests only verify that a plot has been created, not that it looks correct (the Gradescope autograder will manually deduct points for plotting mistakes)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fb98fa3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Project Requirements:\n",
+    "\n",
+    "You **may not** hardcode indices in your code. You **may not** manually download **any** files for this project, unless you are **explicitly** told to do so. For all other files, you **must** use the `download` function to download the files.\n",
+    "\n",
+    "**Store** your final answer for each question in the **variable specified for each question**. This step is important because Otter grades your work by comparing the value of this variable against the correct answer.\n",
+    "\n",
+    "For some of the questions, we'll ask you to write (then use) a function to compute the answer. If you compute the answer **without** creating the function we ask you to write, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "#### Required Functions:\n",
+    "- `download`\n",
+    "- `bar_plot`\n",
+    "- `scatter_plot`\n",
+    "- `horizontal_bar_plot`\n",
+    "- `pie_plot`\n",
+    "- `get_regression_coeff`\n",
+    "- `get_regression_line`\n",
+    "- `regression_line_plot`\n",
+    "\n",
+    "In this project, you will also be required to define certain **data structures**. If you do not create these data structures exactly as specified, we'll **manually deduct** points from your autograder score on Gradescope, even if the way you did it produced the correct answer.\n",
+    "\n",
+    "#### Required Data Structures:\n",
+    "- `conn`\n",
+    "\n",
+    "You **must** write SQL queries to solve the questions in this project, unless you are **explicitly** told otherwise. You will **not get any credit** if you use `pandas` operations to extract data. We will give you **specific** instructions for any questions where `pandas` operations are allowed. In addition, you are also **required** to follow the requirements below:\n",
+    "\n",
+    "* You **must** close the connection to `conn` at the end of your notebook.\n",
+    "* Do **not** use **absolute** paths such as `C://mdoescher//cs220//p13`. You may **only** use **relative paths**.\n",
+    "* Do **not** hardcode `//` or `\\` in any of your paths. You **must** use `os.path.join` to create paths.\n",
+    "* Do **not** leave irrelevant output or test code that we didn't ask for.\n",
+    "* **Avoid** calling **slow** functions multiple times within a loop.\n",
+    "* Do **not** define multiple functions with the same name or define multiple versions of one function with different names. Just keep the best version.\n",
+    "\n",
+    "For more details on what will cause you to lose points during code review and specific requirements, please take a look at the [Grading rubric](https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/blob/main/p13/rubric.md)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c2b70ad",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Questions and Functions:\n",
+    "\n",
+    "Let us start by importing all the modules we will need for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f7f49b0",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:54.590405Z",
+     "iopub.status.busy": "2023-12-06T02:03:54.589407Z",
+     "iopub.status.idle": "2023-12-06T02:03:54.866614Z",
+     "shell.execute_reply": "2023-12-06T02:03:54.865601Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# it is considered a good coding practice to place all import statements at the top of the notebook\n",
+    "# please place all your import statements in this cell if you need to import any more modules for this project\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d7b0e81",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:54.871615Z",
+     "iopub.status.busy": "2023-12-06T02:03:54.871615Z",
+     "iopub.status.idle": "2023-12-06T02:03:55.502591Z",
+     "shell.execute_reply": "2023-12-06T02:03:55.501576Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# this ensures that font.size setting remains uniform\n",
+    "%matplotlib inline \n",
+    "pd.set_option('display.max_colwidth', None)\n",
+    "matplotlib.rcParams[\"font.size\"] = 13 # don't use value > 13! Otherwise your y-axis tick labels will be different."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2100c90",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, you may copy/paste some of the functions and data structures you defined in Lab-P13 and P12, which will be useful for this project."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1437c209",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:55.507591Z",
+     "iopub.status.busy": "2023-12-06T02:03:55.507591Z",
+     "iopub.status.idle": "2023-12-06T02:03:55.513647Z",
+     "shell.execute_reply": "2023-12-06T02:03:55.512632Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'bar_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f66f5eb0",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"bar_plot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "412ec5d1",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:56.044451Z",
+     "iopub.status.busy": "2023-12-06T02:03:56.043450Z",
+     "iopub.status.idle": "2023-12-06T02:03:56.051611Z",
+     "shell.execute_reply": "2023-12-06T02:03:56.050592Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'scatter_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f750fa24",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"scatter_plot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78ad8ae5",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:56.470791Z",
+     "iopub.status.busy": "2023-12-06T02:03:56.469790Z",
+     "iopub.status.idle": "2023-12-06T02:03:56.476150Z",
+     "shell.execute_reply": "2023-12-06T02:03:56.475136Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'horizontal_bar_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41c1c00d",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"horizontal_bar_plot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaabddd8",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:56.667108Z",
+     "iopub.status.busy": "2023-12-06T02:03:56.667108Z",
+     "iopub.status.idle": "2023-12-06T02:03:56.673461Z",
+     "shell.execute_reply": "2023-12-06T02:03:56.672450Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'pie_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f9282ef",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"pie_plot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5377a113",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:56.988463Z",
+     "iopub.status.busy": "2023-12-06T02:03:56.988463Z",
+     "iopub.status.idle": "2023-12-06T02:03:56.995411Z",
+     "shell.execute_reply": "2023-12-06T02:03:56.994394Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'get_regression_coeff' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "127e337f",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"get_regression_coeff\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea47f898",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:57.187939Z",
+     "iopub.status.busy": "2023-12-06T02:03:57.187939Z",
+     "iopub.status.idle": "2023-12-06T02:03:57.195025Z",
+     "shell.execute_reply": "2023-12-06T02:03:57.194016Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'get_regression_line' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8da607cd",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"get_regression_line\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8bfc3d2",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:57.402798Z",
+     "iopub.status.busy": "2023-12-06T02:03:57.401798Z",
+     "iopub.status.idle": "2023-12-06T02:03:57.408788Z",
+     "shell.execute_reply": "2023-12-06T02:03:57.407778Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'regression_line_plot' from lab-p13 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5106a850",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"regression_line_plot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dd24901",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:57.795608Z",
+     "iopub.status.busy": "2023-12-06T02:03:57.795608Z",
+     "iopub.status.idle": "2023-12-06T02:03:57.803382Z",
+     "shell.execute_reply": "2023-12-06T02:03:57.802371Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# copy/paste the definition of the function 'download' from p12 here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fed453d3",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:57.807385Z",
+     "iopub.status.busy": "2023-12-06T02:03:57.807385Z",
+     "iopub.status.idle": "2023-12-06T02:03:57.815281Z",
+     "shell.execute_reply": "2023-12-06T02:03:57.814268Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# use the 'download' function to download the data from the webpage\n",
+    "# 'https://git.doit.wisc.edu/cdis/cs/courses/cs220/cs220-f23-projects/-/raw/main/p13/rankings.json'\n",
+    "# to the file 'rankings.json'\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8811a4ec",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Data Structure 1: `conn`\n",
+    "\n",
+    "You **must** now create a **database** called `rankings.db` out of `rankings.json`, connect to it, and save it in a variable called `conn`. You **must** use this connection to the database `rankings.db` to answer the questions that follow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4a69f72",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:57.820292Z",
+     "iopub.status.busy": "2023-12-06T02:03:57.820292Z",
+     "iopub.status.idle": "2023-12-06T02:03:59.805333Z",
+     "shell.execute_reply": "2023-12-06T02:03:59.804313Z"
+    },
+    "lines_to_next_cell": 0,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create a database called 'rankings.db' out of 'rankings.json'\n",
+    "\n",
+    "# TODO: load the data from 'rankings.json' into a variable called 'rankings' using pandas' 'read_json' function\n",
+    "# TODO: connect to 'rankings.db' and save it to a variable called 'conn'\n",
+    "# TODO: write the contents of the DataFrame 'rankings' to the sqlite database"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3021183b",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:03:59.810346Z",
+     "iopub.status.busy": "2023-12-06T02:03:59.809330Z",
+     "iopub.status.idle": "2023-12-06T02:03:59.843720Z",
+     "shell.execute_reply": "2023-12-06T02:03:59.842690Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# run this cell and confirm that you have defined the variables correctly\n",
+    "\n",
+    "pd.read_sql(\"SELECT * FROM rankings LIMIT 5\", conn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "99b71cbd",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"conn\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d75f6fea",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 1:** List **all** the statistics of the institution with the `Institution Name` *University of Wisconsin-Madison*. \n",
+    "\n",
+    "You **must** display **all** the columns. The rows **must** be in *ascending* order of `Year`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**Year**|**Rank**|**Institution Name**|**Country**|**Academic Reputation**|**Employer Reputation**|**Faculty Student**|**Citations per Faculty**|**International Faculty**|**International Students**|**International Research Network**|**Employment Outcomes**|**Sustainability**|**Overall**|\n",
+    "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
+    "|**0**|2022|75|University of Wisconsin-Madison|United States|83.4|52.8|69.2|58.4|8.1|27.5|nan|nan|nan|66.2|\n",
+    "|**1**|2023|83|University of Wisconsin-Madison|United States|82.4|48.1|70.6|41.9|37.7|23.8|93.2|84.6|nan|63.7|\n",
+    "|**2**|2024|102|University of Wisconsin-Madison|United States|80.2|47.8|61.3|37.4|30.9|22.8|83.6|73.1|83.7|60.0|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a493dd4d",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:00.277311Z",
+     "iopub.status.busy": "2023-12-06T02:04:00.277311Z",
+     "iopub.status.idle": "2023-12-06T02:04:00.302892Z",
+     "shell.execute_reply": "2023-12-06T02:04:00.301874Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'uw_stats', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a3a6df7",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df32cce5",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 2:** What are the **top** *10* institutions in *Japan* which had the **highest** score of `International Students` in the `Year` *2024*?\n",
+    "\n",
+    "You **must** display the columns `Institution Name` and `International Students`. The rows **must** be in *descending* order of `International Students`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**Institution Name**|**International Students**|\n",
+    "|---|---|---|\n",
+    "|**0**|Tokyo Institute of Technology (Tokyo Tech)|31.7|\n",
+    "|**1**|The University of Tokyo|29.2|\n",
+    "|**2**|Waseda University|28.6|\n",
+    "|**3**|Kyushu University|25.6|\n",
+    "|**4**|Hitotsubashi University|22.4|\n",
+    "|**5**|University of Tsukuba|21.2|\n",
+    "|**6**|Kyoto University|20.8|\n",
+    "|**7**|Nagoya University|19.1|\n",
+    "|**8**|Hokkaido University|14.4|\n",
+    "|**9**|Tohoku University|13.8|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dff8ddf",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:00.799683Z",
+     "iopub.status.busy": "2023-12-06T02:04:00.798686Z",
+     "iopub.status.idle": "2023-12-06T02:04:00.813960Z",
+     "shell.execute_reply": "2023-12-06T02:04:00.812942Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'japan_top_10_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "843f8cfe",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a32f7b29",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 3:** What are the **top** *10* institutions in the *United States* which had the **highest** *reputation* in the `Year` *2023*?\n",
+    "\n",
+    "The `Reputation` of an institution is defined as the sum of `Academic Reputation` and `Employer Reputation`. You **must** display the columns `Institution Name` and `Reputation`. The rows **must** be in *descending* order of `Reputation`. In case the `reputation` is tied, the rows must be in *alphabetical* order of `Institution Name`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**Institution Name**|**Reputation**|\n",
+    "|---|---|---|\n",
+    "|**0**|Harvard University|200.0|\n",
+    "|**1**|Massachusetts Institute of Technology (MIT) |200.0|\n",
+    "|**2**|Stanford University|200.0|\n",
+    "|**3**|University of California, Berkeley (UCB)|200.0|\n",
+    "|**4**|University of California, Los Angeles (UCLA)|199.9|\n",
+    "|**5**|Yale University|199.9|\n",
+    "|**6**|Princeton University|198.8|\n",
+    "|**7**|Columbia University|197.8|\n",
+    "|**8**|New York University (NYU)|194.9|\n",
+    "|**9**|University of Chicago|191.4|\n",
+    "\n",
+    "**Hint:** You can use mathematical expressions in your **SELECT** clause. For example, if you wish to add the `Academic Reputation` and `Employer Reputation` for each institution, you could use the following query:\n",
+    "\n",
+    "```sql\n",
+    "SELECT (`Academic Reputation` + `Employer Reputation`) FROM rankings\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65dce186",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:01.349049Z",
+     "iopub.status.busy": "2023-12-06T02:04:01.348049Z",
+     "iopub.status.idle": "2023-12-06T02:04:01.364219Z",
+     "shell.execute_reply": "2023-12-06T02:04:01.363206Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'us_top_10_rep', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85bf6174",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb2d82c7",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 4:** What are the **top** *10* countries which had the **most** *institutions* listed in the `year` *2022*?\n",
+    "\n",
+    "You **must** display the columns `Country` and `Number of Institutions`. The `Number of Institutions` of a country is defined as the number of institutions from that country. The rows **must** be in *descending* order of `Number of Institutions`. In case the `Number of Institutions` is tied, the rows must be in *alphabetical* order of `Country`.\n",
+    "\n",
+    "**Hint:** You **must** use the `COUNT` SQL function to answer this question.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**Country**|**Number of Institutions**|\n",
+    "|---|---|---|\n",
+    "|**0**|United States|87|\n",
+    "|**1**|United Kingdom|49|\n",
+    "|**2**|Germany|31|\n",
+    "|**3**|Australia|26|\n",
+    "|**4**|China (Mainland)|26|\n",
+    "|**5**|Russia|17|\n",
+    "|**6**|Canada|16|\n",
+    "|**7**|Japan|16|\n",
+    "|**8**|South Korea|16|\n",
+    "|**9**|Italy|14|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6446be7",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:01.852740Z",
+     "iopub.status.busy": "2023-12-06T02:04:01.852740Z",
+     "iopub.status.idle": "2023-12-06T02:04:01.866351Z",
+     "shell.execute_reply": "2023-12-06T02:04:01.865331Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_10_countries', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4d31724",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d5f22258",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 5:** Create a **bar plot** using the data from Question 4 with the `Country` on the **x-axis** and the `Number of Institutions` on the **y-axis**.\n",
+    "\n",
+    "In addition to the top ten countries, you **must** also aggregate the data for **all** the **other** countries, and represent that number in the **row** `Other`. You are **allowed** to do this using any combination of  SQL queries and pandas operations.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `num_institutions` containing the **Country**, and the **Number of Institutions** data.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**Country**|**Number of Institutions**|\n",
+    "|---|---|---|\n",
+    "|**0**|United States|87|\n",
+    "|**1**|United Kingdom|49|\n",
+    "|**2**|Germany|31|\n",
+    "|**3**|Australia|26|\n",
+    "|**4**|China (Mainland)|26|\n",
+    "|**5**|Russia|17|\n",
+    "|**6**|Canada|16|\n",
+    "|**7**|Japan|16|\n",
+    "|**8**|South Korea|16|\n",
+    "|**9**|Italy|14|\n",
+    "|**10**|Other|202|\n",
+    "\n",
+    "**Hint**: You can use the `concat` method of a DataFrame to add two DataFrames together. For example:\n",
+    "\n",
+    "```python\n",
+    "my_new_dataframe = pd.concat([my_dataframe, new_dataframe])\n",
+    "```\n",
+    "will create a *new* **DataFrame** `my_new_dataframe` which contains all the rows from `my_dataframe` and `new_dataframe`. In order to use this method, you will first have to create a **new** DataFrame with the **same** columns as `top_10_countries`, but with only **one row** of data. The `Country` **must** be `Other`, and the `Number of Institutions` **must** be the aggregate sum of institutions from all other countries. You **must** then *concatenate* this DataFrame with `top_10_countries`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a50e6e39",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:02.800490Z",
+     "iopub.status.busy": "2023-12-06T02:04:02.799491Z",
+     "iopub.status.idle": "2023-12-06T02:04:02.831577Z",
+     "shell.execute_reply": "2023-12-06T02:04:02.828564Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'num_institutions', then display it\n",
+    "# do NOT plot just yet\n",
+    "\n",
+    "# TODO: use a SQL query similar to Question 4 to get the number of institutions of all countries\n",
+    "#       (not just the top 10), ordered by the number of institutions, and store in a DataFrame\n",
+    "# TODO: Use pandas to find the sum of the institutions in all countries except the top 10\n",
+    "# TODO: create a new dictionary with the data about the new row that needs to be added\n",
+    "# TODO: properly append this new dictionary to 'num_institutions' and update 'num_institutions'\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9ab8487",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "026d1122",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `num_institutions` as **bar plot** with the **x-axis** labelled *Country* and the **y-axis** labelled *Number of Institutions*.\n",
+    "\n",
+    "You **must** use the `bar_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `public_tests.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q5.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "d4abe7f3",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<div><img src=\"attachment:q5.jpg\" style=\"height: 300px;\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71f45ff1",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:04.352427Z",
+     "iopub.status.busy": "2023-12-06T02:04:04.351426Z",
+     "iopub.status.idle": "2023-12-06T02:04:04.821298Z",
+     "shell.execute_reply": "2023-12-06T02:04:04.820285Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the bar plot using the DataFrame 'num_institutions' with the x-axis labelled \"Country\" \n",
+    "# and the y-axis labelled \"Number of Institutions\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e5c8cd4b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 6:** Create a **bar plot** of the **top** *10* countries with the **highest** *total* `Overall` listed in the `year` *2022*.\n",
+    "\n",
+    "The `Total Score` of a `Country` is defined as the **sum** of `Overall` of **all** institutions in that `Country`. You **must** display the columns `Country` and `Total Score`. The rows **must** be in *descending* order of `Total Score`.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `top_10_total_score` containing the **Country**, and the **Total Score** data.\n",
+    "\n",
+    "Your **DataFrame** should looks like this:\n",
+    "\n",
+    "||**Country**|**Total Score**|\n",
+    "|---|---|---|\n",
+    "|**0**|United States|4441.9|\n",
+    "|**1**|United Kingdom|2543.8|\n",
+    "|**2**|Australia|1243.3|\n",
+    "|**3**|Germany|1235.3|\n",
+    "|**4**|China (Mainland)|1138.5|\n",
+    "|**5**|Japan|796.3|\n",
+    "|**6**|Canada|785.6|\n",
+    "|**7**|South Korea|739.1|\n",
+    "|**8**|Netherlands|673.6|\n",
+    "|**9**|Russia|582.6|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73f55d5c",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:04.826297Z",
+     "iopub.status.busy": "2023-12-06T02:04:04.826297Z",
+     "iopub.status.idle": "2023-12-06T02:04:04.839085Z",
+     "shell.execute_reply": "2023-12-06T02:04:04.839085Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_10_total_score', then display it\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2c68ec1",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51185aa2",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `top_10_total_score` as **bar plot** with the **x-axis** labelled *Country* and the **y-axis** labelled *Total Score*.\n",
+    "\n",
+    "You **must** use the `bar_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `public_tests.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q6.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "dbf8c7e3",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<div><img src=\"attachment:q6.jpg\" style=\"height: 300px;\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a92c3a75",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:06.313438Z",
+     "iopub.status.busy": "2023-12-06T02:04:06.312439Z",
+     "iopub.status.idle": "2023-12-06T02:04:06.714496Z",
+     "shell.execute_reply": "2023-12-06T02:04:06.713486Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the bar plot using the DataFrame 'top_10_total_score' with the x-axis labelled \"Country\" \n",
+    "# and the y-axis labelled \"Total Score\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de1479dc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 7:** What are the **top** *10* institutions in the *United States* which had the **highest** *International Score* in the `year` *2024*?\n",
+    "\n",
+    "The *International Score* of an institution is defined as the **sum** of `International Faculty` and `International Students` scores of that institution. You **must** display the columns `Institution Name` and `International Score`. The rows **must** be in *descending* order of `International Score`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**Institution Name**|**International Score**|\n",
+    "|---|---|---|\n",
+    "|**0**|Massachusetts Institute of Technology (MIT) |188.2|\n",
+    "|**1**|Rice University|185.8|\n",
+    "|**2**|California Institute of Technology (Caltech)|181.0|\n",
+    "|**3**|Yale University|168.6|\n",
+    "|**4**|University of Pennsylvania|166.3|\n",
+    "|**5**|University of Chicago|165.6|\n",
+    "|**6**|University of Rochester|163.1|\n",
+    "|**7**|University of California, Berkeley (UCB)|156.1|\n",
+    "|**8**|Johns Hopkins University|155.8|\n",
+    "|**9**|Northeastern University|154.5|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8e84295",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:06.719497Z",
+     "iopub.status.busy": "2023-12-06T02:04:06.718495Z",
+     "iopub.status.idle": "2023-12-06T02:04:06.731585Z",
+     "shell.execute_reply": "2023-12-06T02:04:06.731585Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_10_inter_score', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4aebd42",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71ad92ee",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 8:** Create a **scatter plot** representing the `Citations per Faculty` (on the **x-axis**) against the `Overall` (on the **y-axis**) of each institution in the `Year` *2024*.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `citations_overall` containing the **Citations per Faculty**, and the **Overall** data from the `Year` *2024*, of each **institution**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39556952",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:07.203030Z",
+     "iopub.status.busy": "2023-12-06T02:04:07.202040Z",
+     "iopub.status.idle": "2023-12-06T02:04:07.215728Z",
+     "shell.execute_reply": "2023-12-06T02:04:07.214701Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'citations_overall', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd63aac8",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e867525",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `citations_overall` as **scatter plot** with the **x-axis** labelled *Citations per Faculty* and the **y-axis** labelled *Overall*.\n",
+    "\n",
+    "You **must** use the `scatter_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `public_tests.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q8.jpg": {
+     "image/jpeg": 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Z0VavsThiGMY2iNaAMoxdkds2JSMjQ/E+eeEEkNItZfLz85GRkSGZ1oyMDN4+hGEYJhTRvA+WnXayZ6KDcKEX1OyTF+rar3/964qnwT0eT8AqNX86OjpMrxcsjhiGsRXRFktHybQDCyfGCLzbpng8HtTV1cHj8aC+vh5ffPGFKlET6to333wz7PP9p8FFmzK3fYRshmHExYhYRNG2D1a4EfpLL71kaRwaJjbov22Kmn3y5K4Nh/80uGhT5mw5YhhGd5Q6YUZCXl4eZs6caWthJLf67vrrr1c8tcEweqFGqITyE/JSXFysaBpctClzFkcMw+iOGn+FWEZu1P3GG29w2ALGdNQIlfvvvx8Oh0PyPm63G3/4wx8UT4OLNGXOQSA1wEEgGSY0ogWXExm5vApHXV0dZs6cqXOKGOYrpLb26D+lK1d+m5qaMHHiRADqtpQxcspcaf/NPkcMw+iKGn+FWCfcTvZTpkwJ69DKYQsYI1Hi2ydX17/44gvf//f3a/Ii5ZcY6loz4Wk1hmF0RTTHStEJNZXwhz/8QSgfDCY2CefbF0ldN8MvMRJYHDEMoyuiOVaKTqjl1GlpaUL5YDDKECHsgpFp8L93JHVdeL9E3ba6jSGU7urLMLFKZ2cnud3ugN213W43dXZ2Wp00W+Ldtdzj8VidlIhpbm6Omnfxp6Ojw/Iyb2QaQt27tbVV9TObm5sDru9/GFk2lPbfLI40wOKIYZQRTZ06ExkiiAcjcbvdFBcXF/B+cXFx5Ha7oyINcvdWU9fr6urCiqO6urqI0xsKpf03r1bTAK9WYxjGLhgRiFMLM2bMkHQ8nz59Ourr6y1Llx5EskJTr+9j5CpRve9t5YpWpf03+xwxDMNEISI5vMoFu7R7zCYtW1/o/X2M3H5D73vbwS+RxRHDMEwUIpLDq2j7ZumNllVben8fI1eJGnFv0RcbsDhiGIaJMkSz1ER7eAe1lhAjvo+R1hgj7h1ulaYIsDhiGIaJMkSz1NhhGiVS1FhCjPo+RlpjjLq3qHskskO2Btghm2EYkRFxCxcl21FEA0q2vjD6+xi5/YaR9zYDpf03iyMNsDhiGEZ0RF0dZvfOVS9E/T7RDq9WYxiGCYEIUYyNRlSHV1GnUcxG1O/DfAVbjjTAliOGsSednZ0oKyuL+qkdf9hSIzb8fcyFp9UMhMURw9gTUaYyRAnMyDCxBk+rMQzD+CHC8vZQgf+ampqifpqPsQexMOWsBBZHDMPEBCIsb5cK/NfQ0IDJkydbHsWaiW1EiqguAiyOGIaJCawORBjKctUfq6JYM7GNSBHVRYDFEcMwMYHVgQjlLFdeomW/McY+iDDlLBosjhiGiRmsXD4tZ7nqj933G2PsgwhTzqJha3F07NgxPPjgg7jwwgsxaNAgZGZm4oorrsALL7wQdO1nn32GhQsXYvjw4UhMTMT48eNRXV1tQaoZhrEKK/dzCmW5CoXd9xtj7IOZU852cfi2rTg6e/Ys3G43Vq1ahUsvvRSPPfYYVqxYgZMnT+LWW2/F/fff77v2yJEjKC4uxssvv4xFixbhqaeewqhRo7BkyRJUVFRY+BYMw1iBVYEIpSxXDocj4N9Kpvns0sEw9sCMKWfbOXyTTXnrrbcIAC1dujTg/IkTJygrK4uGDx/uO7d8+XICQBs3bgy4ds6cOZSQkECtra2qnt3d3U0AqLu7W/sLMAwTs3g8Hqqrq6OmpiZyu90EwHe43W7q7OyU/LuOjg5V1zOMUjo7Ow0tW263m+Li4gLuHxcXR263W5f7K0Vp/23bIJD19fWYOXMmHnnkEfz3f/93wG8XXHABent70draCgAYNWoU4uPjff/28vrrr2PatGlYs2YNli9frvjZHASSYRg9URolWZQglkz0YkTEbpE2Qlbaf8ebkhoDmDx5MgYPHoxHHnkEo0ePxmWXXYZjx46hqqoKzc3NeO655wAAhw4dQltbG8rKyoLuMWXKFDgcDjQ2NoZ9Vl9fH/r6+nz/7unp0fdlmKiFIyEzSsjLy5MtH94VRf3xX1GUl5fHZY6JCKmyGGmZUuLwLVpZta3PUXp6Ol599VWkpqbi5ptvxqhRo3DBBRdgw4YN2LJlC2677TYAwIEDBwAA2dnZQfdwuVwYOnQoPv3007DPWrNmDVJTU31HTk6O/i/ERBW2m19nhEeug3n//fe5zDG6olc7ZnWMMS3YVhwBX608ueSSS/DjH/8YmzZtwq9//Wvk5eXh5ptvxtatWwEAJ06cAPCVEJIiMTHRd00oysvL0d3d7Tva2tr0fREm6uCAaozeyHUwTz/9NJc5Rlf0asesjjGmCVM8oAzgH//4ByUmJtKvfvWrgPO9vb103nnnUVZWFvX29tI777xDAGjZsmWS9xk6dChNmjRJ1bPZIZsJR3Nzc4DTYf/D4/FYnUTGpoRyai0uLuYyx+iK3u2Y0Q7fSlHaf9vWcvT444+jt7cXN910U8B5l8uF66+/HocOHcJHH32Ec845BwAkp856e3vR0dEhOeXGMFrhgGqMUYQKYnnnnXeG/Tu1ZY5DBTB6t2NWxhjTgm0dsr2+RKdOnQr6zXvu9OnTyMrKQnZ2Nt5+++2g63bt2gUiwqRJk4xNLBNT2HF+nbEH3g6m/4oij8cT9u+UlrnOzk6UlZUFOH673W7U1NQI24nFOkY54BvVjilZfCACtrUcXXDBBQCADRs2BJw/evQofv/732PgwIEoLCwE8NW86b59+7Bp06aAax977DHEx8dj3rx5pqSZiQ1sOb/O2Ir+QSz1KnPsKycu/a15Ri/6iPl2zJxZPv3Zv38/paenk8PhoFtuuYUqKyvp4YcfprFjxxIAevTRR33XdnZ20pgxYyg5OZlWrFhB1dXVNHv2bAJA9913n+pns88RI4co8+tM7BBpmWNfOTEJFfiztLTU8KCKerZjzc3NVFdXZ3k5Utp/21YcERG1tbXRHXfcQQUFBZSUlESDBg2i4uJievnll4OubW9vpwULFlBmZia5XC4qLCykyspKTc9lccQoxRsJ2eoGgYkdtJa5urq6sOKorq7OoBQz4QjlhG+mkI2kHRMtqnvUR8i2Eo6QzTBMtCFSFGPmK+S+SSjq6uowc+ZMA1KkHtGiuivtv23rc8QwDMPoR8z7mAiI3IqxUIiy6MMb1d1fGAGBUd1FhcURwzAMAyB0qICamhqLUiQWZoc4kFsx5nQGduGiCVk7hzVhccQwDMMAsF8sGrOwajugcNa80tJSXH311QHnRROydg5rwj5HGmCfI4ZhmNjBSr+Zrq4uzJ8/P2Tsqf4xr0TDrj5HLI40wOKIYewP715vLXbJf6Md1ZXmg9EiyKjvISfuzEZx/234urkohJfyM4x9EW1pcaxht/w3KsSBKPlgVjpECWvCS/kNhC1HDGNfRDPzxxp2y3+jLEei5IMo6TALnlYzEBZHDGNPOJaPtdg1//UWEKLkgyjpMBOOc8QwDNMPOy8tjgbsmv96hzgQJR9ESYeIxFudAIZhGLOw89LiaMCu+e8NcaCXU7Qo+SBKOkSELUcMw8QMHAXaWuye/3l5eZg5c2bE6RQlH0RJh4iwOGIYJqbgKNDWwvn/FaLkgyjpEA12yNYAO2QzjP0RPXhetKMk/+0SCykSRCmHVqfDrG/Nq9UMhMURwzCMcXR2dqKsrEyYwIGMcZj9rXm1GsMwDGNLysrKsH379oBz27dvx/z58y1KEWMUon5rFkcMwzCMMHg8HjQ0NATEFAKAM2fOoKGhAXv27LEoZYzeyH3rP//5zxaljMURwzAMIxAceyf68Xg82Lp1K3bu3Bn2umuuuQYzZsxAV1eXSSn7XzjOEcMwDCMMHHsnepHyL5LDO8Vm9lYmbDliGIZhhIFj70QvUv5FDocj7N9YNZ3K4ohhGIYRCo69E32E8i9SumDe7OlUFkcMwzAh8PpGsBOwuXi36/B4PKirq4PH40F9fT0v47cxcr5kDz30UNjfzZ5OZXHEMAzTj87OTsyYMQMFBQWYNWsW8vPzLXMMNRuRBKFe23Uw1iPnS3bzzTcLNZ3K4ohhGF0RqXPViqixV4wklgUhYzz5+fkoLi6G0xkoO/zFj0jTqRwhWwMcIZthgomWqMYejwcFBQVhf49GS8aMGTOwffv2AJ+QuLg4TJ8+3fSVQkwgdt9GJdwqNak2wsitTDhCNsMwphIt1pZYjLPDgRfFoL/VNVqseVJtg9PpRElJiaQvmQjTqSyOGIaJmGjqXGMxzk4sCkKRCCWCbrrpJtsPOEK1DWfPnsUbb7whbNvA4ohhmIiJps41FuPsxKIgFIlQVte//vWvth9wKGkbRPRTZHHEMEzERFvnKpJjqBnEoiAUhXBW13DYZcAh1zasWbNGyGlDFkcMw0RMtHWusRhnJ9YEoSjIWVZCYZcBR7i2ISMjA2+99VbAeVGmDXm1mgZ4tRrDBNPV1YX58+fbfrVarGPkSiEmGLnVkU6nE2fPnvX9244rCKXahuLiYrz55psh/8aoVaExsVpt1apVcDgcIY+EhISA6z/77DMsXLgQw4cPR2JiIsaPH4/q6mqLUs8w0UUsWluiERFWCsUS4SwrpaWluPrqqwPO29GaJ9U2rFixIuzfWD1tGG/p0yPkhhtukDQt/uMf/8DPf/5zzJkzx3fuyJEjKC4uxoEDB7B06VLk5uZiy5YtWLJkCdrb27Fy5Uozk84wUUteXh53rIxp2D0GEPDVlGZ/y4pXBKWlpZlqzTMyP/3bBrlJK8unDSkKWbJkCQGg1157zXdu+fLlBIA2btwYcO2cOXMoISGBWltbFd+/u7ubAFB3d7duaWYYhmGU09HRQW63mwD4DrfbTZ2dnVYnTTMej4fq6urI4/GY/mwr8tPtdlNcXFzAM+Pi4sjtdhv2TKX9d9T5HJ04cQIjRoxASkoKPv74Y5+pctSoUYiPj0dra2vA9a+//jqmTZuGNWvWYPny5YqewT5HDMMw1sIRvfXFivy0wk9Raf9t62k1KX73u9+hp6cHd911l08YHTp0CG1tbSgrKwu6fsqUKXA4HGhsbAx5z76+PvT19fn+3dPTo3/CGYZhGEV4l7/3xz8GkF2n2KzAqvz0+iKJuAjA1g7ZUqxfvx4OhwMLFy70nTtw4AAAIDs7O+h6l8uFoUOH4tNPPw15zzVr1iA1NdV35OTk6J9whmEYRhHRFHRUDjMCJMrlZ01NjaHPF3ERQFSJo+bmZrz55psoLS1Fbm6u7/yJEycAfCWEpEhMTPRdI0V5eTm6u7t9R1tbm74JZxiGYRQTbUFHpTBzXzW5/Fy5cqVQARrNIKrE0fr16wEAixcvDjifnJwMAAFTY/6cPHnSd40ULpcLgwcPDjgYRjREDMEvIpxP9ifago5KoXYj50jKdaj87I8oARpNwTCXcJM5deoUDR8+nNLT06m3tzfgt4MHDxIAKisrC/q7kydPksPhoLlz5yp+Fq9WY0QiGlftGAHnU3TR2dkZtd+zubk54L36H/6r2fQq11L5qeT5dkNp/x01lqM//vGP+Oyzz3DrrbcGTZ9lZWUhOzsbb7/9dtDf7dq1C0SESZMmmZVUhtEVtSPMWIXzKbqI5qCjanyq9CrX/vlZUVGh+PlRizlazXiuvfZaAkD/+Mc/JH9ftmxZyDhH8fHx1NLSovhZbDliREHNCDMWaG5ulowTE4v5FCovGPFRWl6NKtfRXF9iynLU3t6O+vp6TJ48GRdddJHkNcuXL8eYMWNw66234t5778Wzzz6LOXPm4I9//CPKy8sxZswYk1PNMJETS6t2wiHnvBpL+WSmIy9jDEp9qowq11b7dAnhF2iSWDOU1atXEwCqqqoKe117ezstWLCAMjMzyeVyUWFhIVVWVqp+HluOGFGI5hGeHP6WEblIu7GUT1ZEHWb0R4lPlZHl2gqfLjP8ApX231EhjsyGxREjErHWGUo1oEo6iFjIp1gSgbGC3JYiRpdrM7c0MaOO6i6OHA4HOZ1OVUdcXFzELyIiLI4YkYjmVTtSSDWg4Y66ujoiio18qqurU5QXTPQQLeXaLGGvtP9WvH3IbbfdBofDofRyhmFMQuQQ/HoTapuDcHgDAsZCPsVCcEQmELuXa4/Hg5aWFt9OFqHYu3evqe8VdRvPmgFvPMsw1rB161bMmjVL0bWxugkpb8jK2IHOzk6UlZUpHux4PB5dxJHS/jsqVqsxDBMbyFlG/Jk+fTpqamoMTI2Y1NTUYPr06QHnYjUv7Ireq7WEWP3VD6n4TFKzU1ZFPWfLkQbYcsQw1hHOMvLUU0/ZcmrBCOw6zRLLSFlT3G43ampqNAW31Pt+euHxeFBQUKDoWr3Tq7T/ViyOtMQBcjgcsnEY7AiLI4axjq6uLsyfP1+4Bp9hIkXvKVFRp1jlpserq6txzjnnGCLsdRdHV111lSaH7B07dqj+G9FhccQw1sOWESaakLOmqPW50ft+emJl2pT234pXq73++ut6pIthGAbA/65S0Spu8vLyWBQxUYOSaNdqyrve99MTbwTuUFYtEeo1O2QzjE2xq9Mmb2/BMMHoHYZB9LAOwi8c0CWqUozBQSAZK9E7xL4ZIfv9ibZI1bzBK6MXetcNO9Q1MyNwE5m0fcjevXvp+9//Pk2cOJHGjh1Lubm5AceYMWMiub2wsDhirMSMBtTpdFJxcbHOKY+u7S3MFpVM9KN3tOtoiZ6tJ0r7b81L+f/5z3+iuLgYfX19KCgowD//+U8UFhaio6MDhw4dwtixY5Gdnc0O2QyjI2Y7bZaUlGDLli26rQKTW6VSV1eHmTNn6vIsoxF1JRBjf/RebMCLF/4Xw4NA3n///RgwYAA+/PBD/OUvfwEAPPHEE2hvb8e6detw5MgR/PKXv9R6e4ZhJFDiZKnn/f7+979j/vz5qu4ZDtH9IJTi3cbEXxgBwJkzZ9DQ0CBUsD3GfuTl5WHmzJm6CRm97xcLaBZHb775JpYsWYKCggLfEn+vEerb3/42Zs6cieXLl+uTSoZhAJjvtHn27FldO3vvKpW4uLiA81ZFwdWK3iKVYRix0CyOjh496mtYBwwYAAA4fvy47/crrrgCb775ZoTJYxjGH73Fhfd+Tmf4pkDPzl74VSoKsKsFTMRtJBhGRDSLo+HDh+OLL74AAKSkpGDgwIHweDy+37u6uoJMzgzDRI7e4qKmpgaXX3552Gv07Oy9u4h7PB7U1dXB4/Ggvr7eVtGt7WYB4/AJDKMOzQ7Z1113HQYMGIDf//73AIDZs2fjww8/xIsvvoizZ8/im9/8JsaMGYOdO3fqmmARYIdsRgT0drK88sor8fe//x1nz571nWMH49DYaRsTOefxSANy6oUo6WCiF8X9t9blcC+//DJdeeWVdOLECSIievfdd2nQoEHkdDrJ6XTSwIED6Y033tB6e6HhpfxMNMLLfrVhdpwWtciFTygpKbH8m3NYBMYsDF/KL0VbWxs2b96MuLg4zJw5U9NmtXaALUdMNBPOIsUje/shFz7B4XDAvxuwwlrIYREYs9B941l/vvzyS+zatQsjRoyIyQaSxRFjNKKJkMbGRnzve9/De++95zsn6hQSE4hcLKtwf2dG2RN5g1Qm+jA0zlFcXBy+9rWvYevWrZoTyDBMMKI5znrTc+mllwYIIwDYvn27rjGQGGPIz89HcXGx6r8zKxwBh0VgRESzOMrKyoKOM3KMDvAyXftTVlaG7du3B5yzUoRIpccLBzy0D3feeafqvzErHIFdwyIw0Y3mpfw33XQTXnnlFRZIAiCatYHRhmhRl0Olpz88shefiy++WPG1ZocjsFtYBKvgwa+5aBZHixcvxrFjx3DNNdfgtddew0cffYRPPvkk6GCMRzRrA6MN0aYX5NLjhUf24hNOgGRkZAScsyIgZzQEBjUK0Qe/USvatC6Hczgc5HQ6ff8NdUQjIi3lj6ZdzmMd0b6lXHpirYw1NzcHLdmXOicq4UI1iBKOQJR0iITb7aa4uLiA7xYXF0dut9vSdNk1/ILS/jteq6i6//77fXuqMcYTavWSEmsDm6XtgXd0H2pJs9nf0ZueP//5zwGBIfsT7WWss7MTZWVlAcEeS0tLAQB//etffeesWL2nZlWjNzK5VKiGtLQ0Ib5hXl6eEOkQBe/Udn/8p9qtyq9wMxZREX7BJLEWVZhpOZJT56JZG5jIEC0QY2dnJxUXF8d0GZMauTscjqB8MHM0b9dRO6OOurq6sHWvrq7OknTZud9R2n9r9jlijMU7j3vdddeF9SdiZ8boQrR9x9LS0vDGG2+gpKQkaHPaWChjoZzSSWIhil6O80p8OETxM9Tb3yRq/Vc0IupKPtH8Iw0hEgXW09NDFRUVdMUVV9C4cePorbfeIiKiL774gioqKug///lPJLcXFiMtR1IjwlCHV52LZm1goo9YLWNyI3epQ+toXqk1SIRRuxKLthrfIbaEhUZEnyMRyqBWlPbfmsXR559/Tvn5+RQfH0/nnXceOZ1O+stf/uL7fcyYMfTDH/5Q6+2FxkhxJFURlDbC7MzIGI2oZcwox2ilTul6dAxKO0ERplpCpXXatGmaRI6IAkAURB2YRPLNrFzIYLg4WrJkCQ0ePJjeffdd+uKLL8jhcASIox/+8Ic0fvx4rbdXzJEjR2j58uWUn59PLpeL0tLSaMqUKbRp06aA6w4dOkS33347DRs2jFwuF1100UVUVVWl6ZlGiSO1DbFoHRTDmI0ZFgczfI7UjMStHrXLPV9th2n1+9gF0QYmWkSbCBZCw8XRyJEjqby8nIiIDh8+HCSOnnrqKUpLS9N6e0V88sknNHbsWEpLS6N77rmHnn32WXryySfpjjvuoLVr1/qu6+rqonHjxlFSUhKVl5dTVVUVXXvttQSAVq1apfq5RokjpSZ8UUdUdlrWzChH5O9qhsVBqhMoLS2l0tJS3Rp5tdYgKy0tWqYaw4kcESxhjHbUiDYRLISGi6MBAwbQs88+S0TS4uiXv/wlJSUlab29IqZOnUpZWVn0ySefhL1u+fLlBIA2btwYcH7OnDmUkJBAra2tqp5rteVIBJOqPyKMBhj9Ef27mm1xkOoE9BrNq30XK6datEw1hhM5bDmKDUT5zoaLo1GjRoW1HC1atIgKCgq03l6WnTt3EgB6/PHHiYjo1KlTdOzYMclrc3JyKDc3N+j8jh07CACtWbNG1bPN9jmKi4ujkpKSmB69M+Yj+neVszgUFRUJI+SUoCW/rZpqkUqr0+nU3PmJXtaYyBHFQmi4OPrud79Lw4YNo/b29iBx9Pe//50GDBhAy5Yt03p7WbzWoC1bttDcuXMpPj6eANC5555LTz31lO+6gwcPEgAqKysLukdvby85HA6aO3du2Gf19vZSd3e372hrazNMHInqfBcKUUYDjL6I/l2bm5upqqoqbBrt1rnaqe6HSmtpaakmkSPSu4s8jWxnRGlTDBdHBw8epJEjR9KwYcNo4cKF5HQ66ZZbbqEbb7yREhISaPTo0dTR0aH19rJcf/31BIAyMzNp8uTJ9Pzzz9NvfvMbuuyyywgAPfDAA0RE9M477xCAkEItMzOTJk2aFPZZK1eulPyYRgaBFM35LhSijAYYfRH1u6oJdSGKkFOLXeo+UXBaIxU5Vr676NPI0YAIFkLDxRHRVw7Rc+bMobi4OHI4HL591mbPnk1tbW2R3FqWr33tawSARo8eTb29vb7zfX19NHbsWEpKSqLOzk7f9Nt9990neZ+cnBwqLCwM+ywzLUd2Q5TRgBZ4hBgaUb+rmlAXVgu5WMZOAs+L2o6b2w/1iGAhNEUceTly5Ag1NjbS7t27DbUW+TN79mwCQCtWrAj67f777ycAtHXrVlnL0dChQ2UtR/0RaeNZERBhNKAGHiEqQ7TvqtURWGnnxZ1d7CJXtrZt2+a7ltuPyLFSPBu+fcjnn3/u+//U1FRMmjQJkydPRnp6utZbqiI7OxsAMGLEiKDfvOc6OztxzjnnAAA+/fTToOt6e3vR0dHhuxejjZqaGkyfPj3g3PTp01FTU2NRisIjytYLoiPad5XbskDr9iadnZ2YMWMGCgoKMGvWLOTn52PGjBno6uqKOM16w9trGINc2brmmmt8ZYLbj8jJy8vDzJkzxd56SKv6io+Ppzlz5tArr7xCfX19Wm+jmQ0bNoS0CJWXlxMA2r59OxERZWdnh12t9vDDD6t6NluOpLGDKV3U6SKREeW7yn27/hvkKh3Ni2Yhk4KtFcaixCoZFxenehNmtkaKh+HTat/85jdp0KBB5HQ6KT09ne644w56++23td5ONV1dXTR48GDKysqiI0eO+M739PTQyJEjKS0tjY4fP05ERMuWLSNAOs5RfHw8tbS0qHo2iyP7IqqjsT/coIZGTsioFXJ2Ect2EHB2R4s/W6j2g8WsuJjic3Ts2DHasGGDb/mm0+mk/Px8evjhh2UDM+rB+vXrCQDl5+fTI488Qj//+c/pvPPOI4fDQc8//7zvus7OThozZgwlJyfTihUrqLq62uezFMpROxwsjuyLyJ0hN6jy6O3QaRexLGqZjSakypbaw/stWMyKi6kO2UREn376KT388MN0/vnnk8PhoLi4OPra176m1+1D8oc//IGuuOIKGjhwICUnJ/uCJfanvb2dFixYQJmZmeRyuaiwsJAqKys1PZPFkb0RteESNV0iYlVkaiuwg4CLJhoaGsLmd3Fxcdh6aocyZTVRvfFsOGpqaig1NZWcTqcRt7ccFkf2RoTlpP3hBtU6iouLg6I7iyRKuWyYT7iBilz7wWI2NCJYx00XR0eOHKF169bRFVdcQU6nkxwOB40fP16v2wsFi6PoQBRHYyJuUK0gXEBJq8Vyf8ywKuoxmo8WfzklA6hQ7QeL2dCIYB03RRydPn2a/vjHP9JNN91ESUlJ5HA4aNiwYXT33XfTe++9F8mthYbFEaM33KCaT6j9wUpKSqxOWhBGWjv1GM3rbREQRWRpHUCJIAJEQ5Q2znBxdPfdd9OwYcPI6XSSy+WiG264gbZs2UKnTp3SekvbwOKIMQJuUM1DlIZaKUZOR+hR7vQquyJMu+iBiFP3ViOKddxwceRwOGjy5Mn0y1/+MuY+OIsjxgiUNqiijKrtjCgNtVKMEs56iEQ9hWa0DRBEmrq3GlEGJIaLo//85z9a/9T2sDhijCRUgxoto2oREKWhVoKRadVDJOolNO30TdTCA5qvEEH8Gr59yHnnnQcAOHHiBA4ePIgTJ05ovRXDMH6ECq3P2xboR35+PtxuN+Li4gLOK91yxEzktrbYu3ev5nuPHTs27O/jxo0z5R6Ase9pFXbamsYMRNuSKByaxNEXX3yBZcuWYezYsUhJSUF2djZSUlIwduxYLFu2LGDfNYZhIsfj8aChoQFnzpwJOH/mzBk0NDTwXlsasEtDrZf4kEIPkaiX0Oy/N15/4uPjFd1HTyLdy44HNIGkpaWhvr4eHo8HdXV18Hg8qK+vR1pamtVJC0atSertt9+m4cOHk8PhoAEDBtBFF11ExcXFdNFFF9GAAQPI4XBQVlYW7dq1S7PZS3R4Wo0xG7v5yNgJO/iFGDkdoYfzsB73EKmM6zGFHc3ThHbGEJ+jzz77jIYOHUpDhgyhZ555hk6cOBHw+/Hjx+npp5+mIUOGUGZmJn322WfqU24DWBwxZsMNbWTY3efDjNVPeojESO4hUhnXQ4yKJPaY/8UQcfTf//3fNGDAAHr33XfDXtfU1EQul4uWLVum5va2gcURYwUiODPajWhzYreDlSsSRCjjeok0q8WelQMCkQcjhoijCy64gBYtWqTo2oULF9L555+v5va2gcURYwUcO0U9InS2IiFyp0UkRhnX0+JjRfmzckAQybPNKpuGiKPk5GSqrq5WdG1VVRUlJyerub1tYHHEWEm0Ww/0wuqRu0jYzYJmZRnXs9xYIfasHBBoebbZZdMQcZSSkkLPPPOMomufeeYZSklJUXN728DiiGHEh30+/pdYs6BFaoXQO7/MEntWDgi0PtvssmlInKO8vDzs2LFD0bU7duyIaIkpwzBMJBi5BN5OxFIYCL3iCikJ86BmmX+o2GV6Y2WsKC3PFrlsqhJH119/PTZt2oT6+vqw123duhWbNm3CDTfcEFHimOgn0jgiDBMKOwV6NJJoDK4YCr3iCoWLxyMlwCZMmIB33nlHz1fRhJUDAi3PFrpsqjFH9fT00OjRo8nlclF5eTm1tLQE/N7S0kI/+clPyOVy0ejRo6mnp0e9zcsG8LRa5NjNB4KxJyI4+FpNrPhemfWeUtNAIpUtO/kcWVE2Ddtbrbm5mfLz88nhcJDT6aTU1FQaNWoUpaamktPpJIfDQXl5eVG99xqLo8iJNR8Ixlpi3Yk9FuqbGT5mcp25CHlq5YBAy7NF9TnStPHsiRMn6Mknn6Qrr7ySMjIyKCEhgTIyMmjq1Kn0xBNP0PHjxzUl2i6wOIqMWBnJMvKIvrQ8WogFC5oZ7YqcABOpDbNyQKDm2WaXTaX9t4OIKNy0GxNMT08PUlNT0d3djcGDB1udHNuxdetWzJo1K+TvdXV1mDlzpokpYsyms7MTZWVlaGho8J1zu92oqakJuc+Sx+NBS0sLxo0bFzP+QnqzZ88e7N271/A8tOpbzZgxA9u3bw9w8I2Li8P06dNlfWWV4PF4UFBQIHsdt2HqMatsKu6/DZFmUQ5bjiJDzQiPLQvRiRpTeqz6p9mx7Fv9rcywQrjdbnI6ncJbjkTHqvJt6LRarMPiKHLkOkerG1nGONROf8SCv4w/di77Ut/K6XSa/q2MnFKSEmCxUC71wuryzeLIQFgcRY7cCM8OHaIdR/YiIOe3UVRU5CsHseifZoeyL4Xct2pqarI6ibrS1NRERUVFthSxZiHVRlpdvlkcGQiLI/2QGuGJ3iFaPfIxG28D19DQoIsYVLPix4oo15GK3kj+XvSyHw4lojcaCdWGxfLAKVQb2djYaHn5ZnFkICyOjEX0bR+sHvmYhVQDp5cYVOq3YaZYiFT06iGa9S77ZnbSct9K6nupSZ8dBEesDZxCEaqN7G9ps6JtZ3FkICyOjEXk0bPIadObcMHuIhWDnZ2dihtKs8RopM/RI516lS+rOmml31RN+uwkOGJl4BQOJSLZyvaTxZGBsDgyHlEbGausWmaPmpU2cJGkR6kQCOefple+RCpK9BTNepR9q+qP0mkTNekTtS3oTywNnMKhZHqVfY6iFBZHxiNq0DqzG0CrRs1Kg91FKgbVdHz+vh1650ukoldP0Rxp2be6k5b7pmpDedhFcIjuDmAWShzzebValMLiyDxE3PbBzJGsVaNmMyxHRNJCoLi4WLah1DtfRLIcedFa9q3upOXEnZr0Wf0uarCTkDMaJfXTqradxZGBsDiKbcyyalnd2Kr1OdI6xdXR0UElJSWK81PPfPFPswg+R3pgdbnxEqrzi1bLEZE4ZcBqRLX8E7E4MhQWR9GNXCdv1lSX1aPmcMHu/N830vxQ26HokS9SaS4pKaH09PSAcxkZGdTa2qo5v6zqEETvpKPR54hIrDIgAiJa/mNCHIVrILu6ugKuPXToEN1+++00bNgwcrlcdNFFF1FVVZWm57I4kqa/qLDD0lt/lHbyZkUBFmXU7G3gtm3bJvk9I+m8tLyjHvkSyirmcDgi7oRF6BBE76TVpE/0d5FChDKgJ3Zry8MRM+KopKSEfvvb3wYdX375pe+6rq4uGjduHCUlJVF5eTlVVVXRtddeSwBo1apVqp/L4igQKVGRkZFhq8aMSFknb3YUYNFHzZEKFa1WICMFmZVCVG9E76TVpE/0d4lGIrUKiyiqYkYcfetb35K9bvny5QSANm7cGHB+zpw5lJCQoNhs7oXFUSDhfFNE7NClkOswt23bRs3NzVRRURH2Or2jAIs+atYibvwbTK3iKpJ8UboST4lIYxi1iCgYQqF1ECJybKqYEkd9fX3U09MT8rqcnBzKzc0NOr9jxw4CQGvWrFH1XBZH/4vaUbioDYKWDtPMdxR11KxEVHoJ1WCWlpZqtgJpyZdYshwx4iCyYJAiEquwyBbvmBFHAwcO9H2E9PR0WrRoER08eNB3zcGDBwkAlZWVBf19b28vORwOmjt3btjn9Pb2Und3t+9oa2tjcfT/USsqRB2BRxLV1S7vaBRylkNvBxCqwSwtLTW90zDS54hhpBBZMEihdcpbFF/JUMSEOJo0aRL9z//8D23atIleeOEFWrRoETmdTsrJyaH29nYiInrnnXcIAC1btkzyHpmZmTRp0qSwz1m5cqXkR2ZxFD2WIyJl04Nq3tFO5vNICLeqzdsBFBcXy+aZmdYxqTSXlpZSaWmpqSItGpAq57FS9pUiumCQQmuarV5lK0dMiCMpfvOb3xAA+va3v01ERDt37iQAdN9990len5OTQ4WFhWHvGY2WIz0br2jwOSKS7+TlDu872s18rpX6+nqqqKjwTZ01NDRozjurNlSVEmSiTmGKhlQ5Z4EpjYiCQUkd0mLtEl0Ixqw4IiIaPXo0jRw5kojkLUdDhw6VtRz1x84+R0Z03FKiwo6r1bxo7eTlpo/MEIdGjti9996+fXvQ983IyKD169drFkeib6gqRyxaSqTKef9pSbsMjIxGJMGgpg5pXfgg8hRiTIujq666ihISEogovM/RyZMnFfkc9cfO4sjIQtt/xG3nEbicNcybZ/3f0apG0EjRIHVvqWPIkCFhfy8uLrbthqqhEFWsGQ07tatHlLKrJR1q23KRV9nGrDg6e/YsZWdnU05Oju9cdnZ22NVqDz/8sKpn2FUciTR6ER25KbZQFd0q87mRDa8aX6wJEyaETIfdN1SVQoQOL5TVSk9rVv97cTgE9YggGMyuQyIOkKNeHB06dEjy/BNPPEEA6Pvf/77v3LJlywiQjnMUHx9PLS0tqp5tV3Ek4ry36PhXbiUV3YoO3MhnqrUQLF++PGQH4O1gQ0XalkO08mu1WAtltWppadGtEw71jMbGRtXiSKQO0kqUCgYjpmpFq0NWEPXi6O6776bCwkL6yU9+Qs888wz94he/oDlz5hAAysvLoy+++MJ3bWdnJ40ZM4aSk5NpxYoVVF1dTbNnzyYgtKN2OMwUR3qP/rjxMh6zrQlGNnhqLQRe52z/DkCvqSfRyq/VHU2ocpaRkaFb+QtXltnnyBh2795NRUVFuojb/ohUh6zy04t6cbRlyxZyu910zjnnkMvlosTERCosLKR7772Xjhw5EnR9e3s7LViwgDIzM8nlclFhYSFVVlZqerYZ4sgoXwYRpgGiHbPN56JYjlJSUiSfpWeZE6n8WtnRaI3LpWewzKamJl6tpiPhfPv0LONW1yGr/fSiXhxZiRniyKgCLMK8d6xg5ny7KD5H/cuT3gJCtPJrVUejNaK7GmuWUssYh0PQByX1TI/8tLoOWS3OWBwZiNHiyIwRKTde0UW4Bi9S83Woe2/cuJHGjh1LTqczZENn1NSTKOXXqo5GBMuR1XkfTSj9nnpO1VpRh0QoUyyODMRocWS1LwNjPkYENdTbfK0lbIEIjaEZWNHRROpz1D+Ipz/e8qhH+IVoRG9/GaWWQLvXFxH6NhZHBhINliNGDIycfzfafK20oTPDjB6LQRhDWa1aW1vDlqm9e/dKBvFsbW2VLI92DuiqN0bVV7k23+l0+uqLncu6CH0biyMDsbPPEWM9/o2bUd/ZjEZI6TOMnHqy2rlTBEJZrUKd7y92/EVQqPJYUlJi2w5ZT6zy7dM7RIOVWN23sTgyEDPEkZ4dip1HGtGE0kjTeggYs8zXaho6I6aerG5o7UZ9fb3i8mfFqF5kjB5wSLX5RUVF1NTURETRU9atdghncWQgZsY5Uhp4UOoaHlUbjxrhqXbVVyQCxizztd4NnZr8FMFEbzcqKio0i6NY93U0a8Ah1eZHY1m3alEFiyMDESVCtpz4iZaRhoioFZ5W7EVl5vePtKHTIuRFcO60G2w50o6VAoXLun6wODIQUcRRuM4vGkcaIqFWeKiJS6OXgLHafK0GLUKOy7g2QvkcGV0eowGrBpxc1vWDxZGBiCCO5CpLdXU1jzQMQktDpcZypFbAyE1FiRITKBSRNPyxbh3V4k/Y2tqqSiCJKqiNRipvrRxwxHpZ1wsWRwYigjiSs0RUVVXxSMMgtJq4wzVuWgRMtPiURTJlYGRnJfJCBj2+/bZt2+jOO+8Mm/dSMZDkEDnflKAkb60YcNjJEiwyLI4MRARxpGS0zSMNY9Bq6dC7cbPz9/XvQPWYMtCzs7KD6NTz2+t1LzvkmxJEr1eiW4JFh8WRgYggjojkKzGPNIwjkgZUj8bNrj4IoTrQ0tJSRflphlVC9M7RzP3qIl2NKVK+KcGu9YpRDosjAxFFHCkVP6GWhsbC6MOo97RaeGqdirL6u4fqQEtLS8Pmp1lWCTt0jmbsV6f3akwR8k0JvCos+mFxZCCiiCMvcpYI/w4xWkzfcpj1nlaZuNV2RiJ8dyVpDpWfZlkl7NA5miFE9F6NKUK+KSFaRJ7dMXIQx+LIQEQTR6EItU9SuF3Uo4VoMPHLoeYdRciPSKxdZnVYdukcjfyeRqzGFCXflCBCXYlVzBjEsTgyELuII7URme3UgIUjmhrqcCid2hMlP7Smw2yrhB06RyOndY1YjWknrJ4yNxurp9r9MaMMsTgyEDuIIy0Rme1i+pYjWkz8SpGb2hMpP+wQ7FEvB2UzMGJaV5TVmFYT7avCRJhq98eses7iyEDsII7URGSONouKKJYSURApP7R2oFZYJfw7x927d1NRUZEwHYnRmLkaUzTBGSuIZukzaxDH4shA7CCO1FiO7Gj6lkO0im81ouWH2g7UKquE1OhahPwzGjPyWzTLRSwh0oDJ7DSxODIQO4gjotAdYv+tA6KxQTKjcbfTiFfv/LDq3c2e6lDit2eH76+F5uZmqq6upurqakPe0QjBbqc6aSUiTbX7wz5HNscu4ihchxjt8+lejHhPO494I80PO7+7WpRaX6PNh82Mb6y3lSCWyqUeiGg5IjJnUMviyEDsIo68xIoQMgvRpqjMJJbevba2VpE4irZ6ZcY31ttyEUvlUi9EzrOGhgaqqKjQtLefHCyODEQEccTmY2uor6+PqY7SH1FHm0ZRXFwc9n2dTqcQHYmemPWN9XxOrJVLvRBxdaFIcY6cYITC4/Fg69at2LNnj+TvnZ2dmDFjBgoKCjBr1izk5+djxowZ6OrqMjml9kcur/3x5vuMGTPCXrd37169kiccLS0tYX8X+d3VfGvv9W+++WbYa66++mrU1NTokTxhMOsb5+fnw+12Iy4uLuB8XFwc3G438vLyFN/LzuXSStLS0lBfXw+Px4O6ujp4PB7U19cjLS1N0/3U1jEpysrKsH379oBz27dvx/z58zXfUzO6ybEYwgjLkVLFLLIp1Gy0Ws+0jE6UBtSM5lGqHUfoWkeictM+a9euNekNzMXMb6yX5cKO5TKa0Mvaw6vVogAjxJES0cONwFdEWhnVCkwljrmxIlDdbrettp/ROpiI5bqmpX5EMsWvh0+kUYNGdl+QR6+85zhHUYDe4kiuIfY6pYm6/NJsIqmMWjo9JQE1rZ6rN4OOjg4qLS0NevfS0lIh3z1SgROrVlqlFh2RVojp6T/T3NxMtbW1QT5nsVDHpQgnEO3oO8biyED0FkdKO9/GxsaYHc16ibQCaRGYSsVrtGM3sRDpYEJEh1UzkbPoiFgeIrFChQv4KcK7mY0S8WvHVYcsjgzEbMuRfwERsUEyk0gro1xeV1dXU0NDQ1ADK3K+m2H6t+M0k15p5lAYwdixPMjBfoWBWOHqwXGODOL48eOUm5tLAOg73/lO0O+HDh2i22+/nYYNG0Yul4suuugiqqqqUv0cs3yOpI6mpqaYHs3qURmV5rV/3opoRTBzWsOuU7oii1o7Y9fy0B/vwKKhoUFRe2Cnd4sENe2sEXXMyAFJTIqjH/3oRzRo0CBJcdTV1UXjxo2jpKQkKi8vp6qqKrr22msJAK1atUrVc4wQR1Kdb7iKGcuj2Ugro1ReOxwOyfzuf1+R8t3Mjt+ulgIRRa0d6W+dtGt58CI3hWbnd9MDNeLXbnUs5sTRe++9R3FxcfToo49KiqPly5cTANq4cWPA+Tlz5lBCQgK1trYqfpaRQSDlRjCxUDHl0KsyejweqqqqsmWDaEXnZGcrjEiilkjeyVWUtIazTtq5PKixHtvt3fRAS/siWh0LRUyJo9OnT9OECRNo1qxZtG/fPklxlJOTQ7m5uUF/u2PHDgJAa9asUfw8oyNki9boGN1Ya72/HpVRiTN8/5GSCFgxrWG3EWI4rBIg4cSGSKu/vIRri+xaHpTumdf/sMO76Ylo/ZBexJQ4evTRRykpKYlaW1slxdHBgwcJAJWVlQX9bW9vLzkcDpo7d27I+/f29lJ3d7fvaGtrM1QcidLoGN1Yi9AZKG0oRRsNWTmtYZcRohRWl7lwHY5onZHSMiZSeVAiepUOiLzHf/3Xf1FTU5OJbyEGovRDehMz4mj//v00cOBAevjhh4mIJMXRO++8QwBo2bJlkvfIzMykSZMmhXzGypUrJSuN0XurWd3oGN1Yi9IZyJnYS0tLTU2PUkTJPzthZZ5ptVhYJc7t5HStRvTKfQc7BTk1A6v7Ib2JGXE0c+ZMuuCCC+jLL78kImlxtHPnTgJA9913n+Q9cnJyqLCwMOQzzLYciYDRlgmRHDrDOcM7HA5hxVG0juyMwuoyp9Zi4X/U1tYamjYprM4vNagVvVLX9xdFIr+vXRDJf85LTGw8+9JLL2Hr1q2orKxEQkJCyOuSk5MBAH19fZK/nzx50neNFC6XC4MHDw44oh2jN3MUabPItLQ0PPnkk5K/ERH++te/RrSZolFIbRz55JNPYteuXUKm12qsLnNjx47V/Lff+c53dNlcWmpz0FAbhuq5OayReDweNDQ04MyZMwHnz5w5g4aGBsm6UFNTg+nTpwecu/jii8M+hzewVY6aDdL12LDWEMzRavrT19dHw4YNo9mzZ9O+fft8xxtvvEHAV/5F+/btoyNHjoT1OTp58qSsz1F/jHbIFoFYshwRkeyqNZGmEKQIN60QyehNxJGfVkQoc+EsHP23q+h/lJSUaH6uVPkoLS0N2g6mv+XRDtbJSKb//KeMRCgf0YISS55V/n9RP63W1dWlyBztXYWWnZ0ddrWa12dJCbEgjohiw+dIabwT0RvGUNMEGRkZmhofqx2XjcLqMhdObNTW1hpWDqXeWyq2V6i8ENnvRE9RY3X5iAaUfg+r8jrqxdGXX35JmzdvDjrWrVvna3A2b97s+xDLli0jQDrOUXx8PLW0tCh+thniyIgRu9p7Gj1qFGFUKueMbYeGUY2jr9L3idZOQoQyRyQtNpR8Ry0WTC2O4CKKoHDoVV5FKR92Roklz0orXdSLo1CEinPU2dlJY8aMoeTkZFqxYgVVV1fT7NmzCQjtqB0KI8WRESP2SO9p9KjRqlGpkk5D74bRCNGrxdFXav84/zRGU8cphTcAaHV1tVDvIze1piWtWsqH6NPI/dFb1IhsKRMdJe2HlSshWRxJ7K3W3t5OCxYsoMzMTHK5XFRYWEiVlZWqn2GkODJixB6tVoBIkaug1dXVuj1LL9ErJa4iWSIulQ47LeHWwu7du6moqCgoDxobGy3vEDs7O4OmQiOtr7FgOfLCokYM5Pqc3bt3W1b+YlYcmYFR4siIEXssWAG0YmbeRCpQ5cSVlu0QQqVD9DKj1fqmZj8tK6dSOjs7qaSkRNf0ROpzxJiHf/m264IIOUue2+0OuZ8l+xzZGKPEkREj9mi3AkSKGVY1PcSGXDqlGqOMjAxVgsnonbYjJVLrmxoBafW7EulrBZEqH0pWqynBrh24aMiJdzv6PmnxrevvF6w3LI4MhC1HxqBXI6vmPmY4YEYqUNV8Q//GqLOzU9aHJVQ6RHRM1SLYvGVBbkNnO9QPPZDqrLSKMJFXNNpRsEXD4hAlKPGBM7IcsTgyELN9jpxOJxUVFem6jFekiqZXIxvJfYz0VYhUoGoRV1J5MWHCBNXpEMWHQ20eqplCi0S4xjIitisiC7ZwqPELs7ouRoqSdzWyHLE4MhAjxVG4rSy0VnQRrQD+6NXIithY65E2LeIq1POkptpEyaNwqBWIkfhg2aUjstI6IqpFWuQ2IBxqVhRGg2BXWj+NKEcsjgzEjDhHHo+HioqKdK3oSq0AZja6ejWyojbWXiIVqFIrmICv/Ir6I5cX/afaRBLKoVDzfbWszurvHKpHh2pUPRLBOiKiL6PobUA49LYciT6t2NjYGLRi1KxyxOLIQMwKAml2RTe60fX3//BWXL0aWbn7FBUVCSEAtExTqS0LSvJUlOkyNSi1CqgZhRcVFdFf/vIXXcu90fVIBOuIiEJERMGmBj18jkQQzuFQO93NliObYYY4sqKiG9XohqsQegW9k2usQ72H2VYyLc9SWxZE7Lj0QKn1Te79t23bJvkd9BKMRooXkb6tCCLNH7PzRu+2Qw+XCtG+SX9ECCnB4shAotFyZOTzwo2I9PSDcbvd5HQ6Fb2HmSOsSJ+lp8+RKI1kJCgRMeHe30hBbHS9Fck6IqIvoxnl3ui2w798qxHsIglnLekzqxyxODIQszaeNbODM6rRVVoh9PCDaWxspLFjxyp6DzPzVo9nqb2HiB2XmUi9/7Rp0yKK66NEVBktXqzsAEO9v0hTtGaUe1EHHiIJZy3pq66uNqUcsTgyELPEkZkdnFGNrlL/j0j8YNTMY3ujzprVwej1LK1lQaSOywr8319rp6bGUmBG2TK7cxbdj0UKo8q9yNYZkdMmUvpYHBmIWeLIi1kdnBGNrlLLkdZ3a25upqKiItnpNP/3MHOEpfezokHsWLGSJpKGWW29MFq8mG0VFNVSYgWiW2dE/1YipI/FkYGYLY78MbJjMarRlfM50lIx1K568H8PO1qO9MSqZb5WWiC0dmpavp9Z4sUMoSxi+bUS0fPD7NkGteVPhOl+FkcGYoU40sOpV2lB1rvRDbcKQ2vFUBpErKKiQjJ6slTcIKfTKazPkR4YIU7UlCsr80FrpxaJpSAarHyiW0qsQJT6HA4jy54e7YiVdYPFkYFYIY7M8JcwGm+FCLWUWimRBkwLJawyMjIMyRcRRktE+jbqasuVCCNuLe8vQrqtJNbfXwpR6rNV2EEchoPFkYGYLY7M9JewA0qcvEO9Y319vWWNfUNDA1VUVNC2bdsMe0Yo9O7k1JYrESwQWjs1q+qQKFGOpUJk2L0N0YNosAyqxSyxbGTZZ3FkIGaLIzP9JeyAEstR/05PqY+SEZ20CNY7PcWJlnIlUllU26npaSlQ0uiLUF686WxsbAwKfwCASktLY8ZSYgfMEtJGD3LMKPssjgxEdMuRt6JUV1dbPlo3ilCj+aKiIlVTaWZ00mZYHuQaRz3FidYG0u5WzEgsBXKNvv/3szKflAwi7PTNoh2zhbTWgZFIfoksjgzESHEUqiApKTQdHR2y23FYNVrXGzWjeaU+Skb4HBltMVHTOOrV8Gh9J6lvVlxcTLW1tbYui0oIlffTpk1TterSinAe0dh+RAtWCGmlzxTVL5HFkYEYIY7kClJLS0vQCquMjAxqbW31/X2ondujeeSnZDSvNBClEflitBlaTeOo5/RQJI2yx+Oh2tpaKikp0SUtoqNk3z+l4shIa6+ahQ5Gp4WRx6qpaqXtiKh+iSyODMQIcSRXkOR+V2MxitZOKBRqG/1IGxV/65+RDZjWe+vhSKpmE1itllBRHJIjRak4N6Ns6plOu38Xu2P1Iodw7YjIfoksjgxEb3EkVygaGhoi+h0wb98apZjd8U2bNk1xo6+1UQll/SstLTXE9C3XOBYVFRkugkM1kOEsoXLlvbGx0XKHZD1RK86lDrNWx4mSFpEQVaSLtMihPyL7JbI4MhC9xZFcQaqoqAj7+/Lly2UbNFEqtlUrcUpLS8nhcChq/LXmVaiKXVpaasg7K5musaoTC+djU1RUJCvq7Oy4LYUaXx6pw8jVcf7XGpkWuyHCqkE5RF3koHUR0fbt28O6j+gBiyMDMdty9Otf/zrs7xMmTAj7e3FxsS7p1AMrKrPSRt/pdIZc7RbpMzwej6LpLLWjVKkYNFYLY7m8kEuvSO+iF+GixPc/vPXByNVxUigZpNk1/4m01S0RhYc/IgekVLqIyOzVkSyODMQsnyP/IzU1VVYAmRn1WQtWmYG1+HyobWAinf/XOkrt7OyUtcSY7Tir1cfG4XDQ+PHjhXoXvVEyBa5H56alYxd5miYStNQtu+WFiAEplQg3K1ZHsjgyECPEkZqRpdRRW1sb9PclJSXCCCMi6xwI5Rq6wsLCiEeIkTamkYxSRWvItfrYDB48mC677LKw1zQ1NZn6LnojVweqq6sjfgZH1A9EyzuZ0VaJ6sukN6GEm1WrI1kcGYhRcY4icdz0FjyrRhBKKrqVnXioBlJulZ+aNGntWPTIF9E6Nan0KPX5CnfYpZMOVR/MqAORdOyiTdNEKiC05reR38kOvkxmYNXqSBZHBmJUnCO56REg2F/D6lGd2opuVSceqtGvra3V3JEofYZco6fHKFW0Tq2zs1NyheDAgQMjFkgij7SV1Aej64AeHbvV0zR6CYhI6pZR30m0gYyZqAlzYlTesDgyECt8jrxHf0uH1SMOtRXdjE483Gizf6NvxAhRbceye/du3dJgdafmT6QrtPQQrGajpD6YUQfs3gHrlf5I6rcR30m0KXCtqLXoqQlz0t/CrHfdYHFkIGavVvMXRkTidICRVHQj3kHraDPUii+znNndbnfIKSe7dGb9UTtF7O34zF59p6ffh9r6YGQ9NtuSaGU+yqFWaPV/Fz2/k9WBGyMlkjZWTZiTpqYmw+oGiyMDMTvOkfeora3V5Xl6IVpF1zra7OzslNx6RYTAe3Z1QFbrT+BtYMMtTNDze6hp5JV2/KLVByLjB1JG+M/onY9KhaIZvkB2txwZtQrSzAF/1Iujjz76iMrKyui8886jwYMHU3JyMp133nl0zz330MGDB4OuP3ToEN1+++00bNgwcrlcdNFFF1FVVZWmZ+stjurr6xV1IKJVHD2ngyJFrgJWV1db4nwph4gdKpHxjrD9v01/mpqagnzw9OyotMZgCZcGu3d8WjBi+s6ofJTrgM2airTrlKfW7yJaGxf14mj79u1UWlpK5eXl9Mtf/pLWrVtHP/jBD2jgwIE0YsQIOnTokO/arq4uGjduHCUlJVF5eTlVVVXRtddeSwBo1apVqp9ttuXI6XQKWXHkVnqZafVQaqmQ6tysrLyidah6jp6V+hyZPf2qNM+1dGJ27fi0YGTZNTMfm5ubqaqqyrR6KMLiCS2DH63tpFw56R9yxugQB1EvjkLhXX20evVq3znv9hobN24MuHbOnDmUkJCgOjS52T5HxcXFvj2pjDY9Kn2GEstAUVGRYenUkp5QDazVAkWkDlXPtMjF7lIa5kDvMq+kkddaJkTo+PRCLu+NHFSYkY9KojPr8S6hsMJ3NJLBj97xs/q3A2aFOIhZcdTY2EgA6Mc//rHvXE5ODuXm5gZdu2PHDgJAa9asUfUMs1arOZ1OKikpMaXQqH2GUkuNmRVfzeooLbvEExnTWUt1BF5BbCZGTmfU1tZSSUmJ4vJlZJlX8p6RdvxGd3xGDpSU5r0Zgwoj8zGS9sKuRDr4icSvU26moaSkxJRBYsyIo5MnT9IXX3xBbW1t9Oc//9m3z9hf//pXIiI6ePAgAaCysrKgv+3t7SWHw0Fz584N+4ze3l7q7u72HW1tbbqLo3AjJTMsC1pWdChpVMycT1YTZbx/uuRGqkYL1I6ODlXiwQjMmF4MFy3X/7zRZV7u/lZbE0NhxkBJTd6LZPVUg1UxdqxEjzIdiUVP67ZCete3mBFHTz31VEAm5uTk0PPPP+/7/Z133iEAtGzZMsm/z8zMpEmTJoV9xsqVKyU/mN4RsonMicPTH63PMHvZtVI8Ho9mH4JQnbdRnYBXFEjtjWd2w2yFINi9e3eQ47WeUctDoXXfJ6s7S6PTpLYM2HUaMRIfRbui5+BHi0Uvkh0g9Bxkx4w48lqMNm/eTPfffz9dcskltHbtWt/vO3fuJAB03333Sf59Tk4OFRYWhn2GGZajUKgp0FpN7VorjVnLrrWiV0dihGhQ4+9g9dSkEd8y3PvLCW49G8pwjbxVHb8dtx4RJfaaUiJZ3WpXRLCGhmpfvDM+oY5t27bploaYEUf9+fDDD2nAgAH08MMPE5G85Wjo0KGylqP+GLW3mhRKCnSkpvZIK02oZdeNjY2WNph6dW5GTDep8XewemrSCEGg5v2tFItE5nX8cvXYrM1QRcr7cETqdyWiZdBorH5nM7ZxkiNmxRER0aWXXkrZ2dlEFN7n6OTJk4p8jvpjpjgiki/QehR4Pe7h7UQaGxuFMrVH2rnp3WGoNS9bNTVplCBQ+v5G7CMo8k7oovhBWd2ByqGX35VdpwQjQZR3tsJ9xEtMi6Px48dTcnKy79/Z2dlhV6t5rUxKMVIcSTXe4Qq0XoVKajWB1kojeuOqheLi4rCdtdR3855raGgI+E2pv4OSyLOidvThUPr+eu4jqCWgo5l5a2TsJbWIsnoyFHrngWhTgmaUPT3fWa/0mtVvRL04koqCTUT017/+lZxOJ5WWlvrOLVu2jADpOEfx8fHU0tKi6tlGiCOpxru4uJhqa2vDWmL0MLVLPbt/YC6lRCLWROzsw/nGuN1uamlpCfq9tLSUSktLQ/6NN9yE3BGq8zYrHohRyJUR/6CnejXiShteq/JWrh5XVVVRXV0dNTU1mZI+EVZPSmGnaT+12K1e651es6xaUS+Orr/+err00kupvLycfvWrX9HatWvp1ltvpYSEBEpNTaX333/fd21nZyeNGTOGkpOTacWKFVRdXU2zZ88mILSjdjjMinMU7vA27Ho0Fnoqdi1iTeRGQcpi5J9GJbtKS+VrqDwvKSmRFQPRYJkLV971/vZq6ohVeatmqtXtNnZjTiJxy5hoW1Hoiah5Hgqj0mu0JS/qxVFtbS3NmjWLsrOzyeVyUWJiIhUUFNAPfvAD+vjjj4Oub29vpwULFlBmZia5XC4qLCykyspKTc82O0K2XMMeSSE1259GahWIiI1CR0eH7JJyuVVV4Q6tFoBoGTlLjRKLiooM2XJGaYdqdd6GEoz9xbb/wMiITsTqfLBr2iLBbu9lt/T6E/XiyErM3lst3FFVVUW1tbWa/TPknl1RUaG6oCuxguntMyXXUajpSJqbm6moqCisBSjSw9shqx0lRdvI2Qx/D6VlzOq8VRPENFR90gOr80EOEQdTkSJ6nvfHbun1h8WRgYhkOfI/JkyYQMuXL1cVE0Lps9U0vlKNfKjRb6TiTG5KTs2UnZrYQ5Eeoqyck7q/aH5feqCkQxVlNOwVjNXV1YrLk56bU4uSD6EQZcWVnoie5/2xW3r9YXFkICL4HEkJDv9DzRSF2y0f6VrLyExJpOqGhgZF7xpqtYyeYQ60xt7pn3dKfI4iQa+Rs78QEtnvSw+Udqih6kJGRoZwe90Z2SnZwToj2iqzSLFDnvtjt/R6YXFkIEaIo0hM6uEOJR1cZ2cnZWRk6N74Njc3U0VFRdj71dXVKRYl/Tsouc5DTnj5v0t9fb3qvI2Li6PS0lJVq9X0WBId6chZSghlZGQYEldINOQ61FB1wel0UlFRkekdsVrBrtd0RjRaZ0THbnluVHqNtl6zODIQI+McyXXoDz30kCpzu5IOTs0IVWtogFBHU1OTKmFYUlLie46SKTm5d4lkKs2/IZDqdL270U+cONGQBk/ryFlth2vVyNyKKT4ldcHMDkvtoEnvuDXRZp2xA3bLc73Sa5b1msWRgRgdIVuu85JbRSV1hNsrSI1DuNbQAFKHw+EIEG4ej0dW0PinQQ/LkRqhkJGRoXoJtZzp2SwB4H3O+vXrVZcds50rrZziU1IXrPh+Sqaoi4uLI3pGtE+tMmJj1jQdiyMDMVocyY0W4+LiKCMjQ5N/jFRjp2S0rFdoAKnD34Fcyd/7d9aR+Bzp4dMRrnOUu3+kQfaUdMx6OJmLMJVk1hSfmjJhRZDEUGJeD5+oUPleXFysaqWnnawejDkoWU1sVhvE4shAzNpbTc7yocWCpNUZWWnDL7eBoNz9m5ub6YILLlBcUeTmvcP9riWEgleYKRlly92/v4+PUr8WNSN8Jc724Q7/SPN6IFIjGQql1kQrfLSkyrPWaPb+RLpq1UirEwsuMZEbGKrZZ9PM0AAsjgzELHGkpMB4/Vr6j2Lljv4FOlSjW1tbq6pR0iLYvB2NnFN4uOXKcvPeUr9HYjnSY2m4lk5I6bOJiHbv3q35+cBX0556iSOlnacI8VMiXRwRrrPQq4PX2y8l0v3+jLD28TSfmIT7Lkos1VJBTNlyFCWYJY6UFhhvIdu4cSMVFRUpauRCdTKRNLp6xWsKdYSbOtDa+ajxOfL6dES6HYUSS06ojkXNs5WWBS2dvVqUdp5K/MjMsiJ4PB4qKipS/f3865ZdOni1dVfNIEPrtwpVdyL1rWIiI9wm3FrDobjd0lsxsc+RzTBLHBGF71RCNbxNTU2yzptaxU+onee9oircM//rv/5L94460s5HjZWgtraWiORH2dXV1WHvr8a61l8Ay61UVLoVhv/x0EMPKbqnXFkIV27UfNNQnWJ/y6IZIkPL9xNhrzYtqOnY/MuEEdY+Jf56ognMaEfJlkpaj1BhUXi1ms0wUxyF85mJxBlZDVICRCqWj1zFaWhokO2I1TTKeryjt5Pftm2bYkGpZbl3f4uc0o6otrZW01JuNauu1IgXLWJUbecpVealFiCYKTKUfD9RI24rRc1AQU/LkZTQVuKvJ6LAjGYi9V9UWq5441kbY6Y48tK/wChpkPQK0iVVKaSiQIdaRafEn0ht5680D0IRqpNXatqVEzdyHbfSjqikpCToOaHyXq2/k39ZUCoytYhRrd/JW+bVBPMMh9qp13DXNzY2Bk1bqnXIF3X/KW++S5U9PX2OwgltpZZP0QRmtGK0y4SZdYLFkYGY6XMUahpL6fQKUWR+RFqceidMmBDwbynBpHZTV6mGVq7zqaqqCvneoRpzpaZdpeJGLs9D+bV4l1ArzR+pNIaanpJaEadESEciRiOx8EUqMtRau9Q6nIbaqsdulqP+qBlchZp+DLegQ4nlW41/F2McSi15Ut9UTVtvRp1gcWQgRosjqQZ42rRpIbekMLKQaXXq9a50U7p3mpbOX81oxv/vlXRaSgWlGpEailCdkFxYhOrqatmtMNRaDsO9dyQiJRIrZqQiQ60wC3e9nvfq/46iLldXM7gKtXpWi9Du7OxU5d9lZ0T+/kTy38u7LVKoeu4fPNdqPzwWRwZilDjyVpDi4mLJKZv+ClzJ9Eqk6dEqZrzp0BJLSGnnTxR6eqt/3vjni5zoqKioUDX1olfjrWXqVMt9taLHSjKtadHaoKrNw0jKvNQ7yYlCu6xmU4OSb6VGaJeUlETtvn92+v6hLNH+Wzp5CVfPrd5DjsWRgegtjvSIYmxEIYtE2Ph3mHp2Nv3RsveU0ukqpXlp5EjI6lFWf6SW8ZqxkkxLg6p042N/Iinz4SxnoToL0b5vpKgJP6K07lvdmRqJnb6/mu+gxBJmtON1KFgcGYje4kjpyqVQhxILixqam5upqqqKli5dGva5Spbm19XVhWwAEhISwgoTNXgrmtw0l9yKNC2NlJGNt9UdQ7hIt97DzJVkShpUNYMNoy1H4bC7T5IUaixCaoWBVZ0pkfJpLyNDXIhCuO9gB0sYiyMD0VMc6bEKQI/pEm8HqMavSelKIi2xYqQcXPXIz8LCQsPy18jG2+yOQYnAcDqdQc73IjTwSgYbZvkchcOuq9nCoabDb2lpCbI4ZmRkUGtrq4VvEIjSzt6MEBd2wA6WMBZHBqKnOIrEjG9EeH4tokFphfDv4I1sGEKlR+tmvXZspCIlUmumVXkX6R5hROGtdXpa8uxqOZBDaXtgh47UyHeJtu9vl/dhcWQgoliOIjVXau0A+1d6rf4gRlWkSKNSi1qpzULPmCZm552c6FbjbB/OWqeXJc8OAkEtRoeFMAsj/Kf6E03f3y6WMBZHBmKVz5F3ZYAZK4+0iDK1HYbRDYMaS1VKSkrUNFKRotaaKbW60qq8s0On64/VPmVGYlRYCLNQmkarQlyIhl3qHosjA9FbHCldcWX1SrTq6mpdC7iZDYNcxW1qaoqaRipSlApnrwASrYG342jcSmdjK7BDR2qG5chLtHx/O9Q9FkcGYlSco1AB1EJF4I0EtZYjIwu3WQ2DkoobLY1UpEjlVf/YUf0FkCh5J5pYY6SxQ0caTf5TZmCHusfiyEDM2D7EjI5G6XReaWmpUIVbK3aouKKgJNKt6Igi1hhp7FAflabRDu9iJiLXPaX9t4OICIwqenp6kJqaiu7ubgwePNjq5Gimq6sL8+fPR0NDg++c2+3GQw89hA8++AAAMHXqVOTl5VmUQmPYs2cP9u7di3HjxkXdu+kN5xVjNHYoY0rTaId3iXWU9t8sjjQQLeLIC1dohmEYJhZQ2n/Hm5gmRlDy8vJYFDEMwzDM/8dpdQIYhmEYhmFEgsURwzAMwzCMHyyOGIZhGIZh/LCtOPJ4PLj//vtx2WWXITMzEykpKbj44ouxevVqHD9+POj6zz77DAsXLsTw4cORmJiI8ePHo7q62oKUMwzDMAwjMrZ1yP71r3+Np59+GnPmzEFZWRkGDBiAHTt24Kc//Sl+97vfYdeuXUhKSgIAHDlyBMXFxThw4ACWLl2K3NxcbNmyBUuWLEF7eztWrlxp8dswDMMwDCMKtl3K/84772DcuHEYMmRIwPmf/vSnWL16NZ5++ml8//vfBwCUl5fjZz/7GTZu3IgbbrjBd+3Xv/511NfXo7m5Gbm5uYqfHW1L+RmGYRgmFlDaf9t2Wm3ixIlBwggAbr75ZgDAP//5T9+5F198Ebm5uQHCCADuuecenDp1CrW1tYamlWEYhmEY+2DbabVQHDhwAAAwbNgwAMChQ4fQ1taGsrKyoGunTJkCh8OBxsbGsPfs6+tDX1+f7989PT06pphhGIZhGJGwreVIijNnzuCBBx5AfHw8brnlFgD/K5ays7ODrne5XBg6dCg+/fTTsPdds2YNUlNTfUdOTo7+iWcYhmEYRgiiShzddddd2LVrF1atWoWCggIAwIkTJwB8JYSkSExM9F0TivLycnR3d/uOtrY2fRPOMAzDMIwwRM202k9/+lM888wzWLx4MVasWOE7n5ycDAAB02L+nDx5EllZWWHv7XK5AsSV14edp9cYhmEYxj54+225tWhRIY5WrVqF1atX47bbbsO6devgcDh8v51zzjkAIDl11tvbi46ODpSUlKh63tGjRwGAp9cYhmEYxoYcPXoUqampIX+3vTiqqKhARUUFvvnNb+K5556D0xk4U5iVlYXs7Gy8/fbbQX+7a9cuEBEmTZqk6pkjR45EW1sbUlJSAoSYGnp6epCTk4O2tjYOB2AwnNfmwXltLpzf5sF5bR5G5jUR4ejRoxg5cmTY62wtjh544AGsWrUKt9xyCzZs2BAkjLyUlZXhkUcewaZNmwKW8z/22GOIj4/HvHnzVD3X6XRKOnhrYfDgwVzRTILz2jw4r82F89s8OK/Nw6i8Dmcx8mJbcfTLX/4SK1euxKhRo3D11VejpqYm4Pfhw4fj6quvBgAsX74cr7zyCm699Va8++67vgjZr732Gu677z6MGTPGildgGIZhGEZAbCuOmpqaAACffPIJFixYEPT71KlTfeIoLS0Nb775JlasWIHq6mr09PRg3LhxqKysxHe/+10zk80wDMMwjODYVhxt2LABGzZsUHz9iBEj8NxzzxmXIJW4XC6sXLkyZIgBRj84r82D89pcOL/Ng/PaPETIa9vurcYwDMMwDGMEURUEkmEYhmEYJlJYHDEMwzAMw/jB4ohhGIZhGMYPFkcMwzAMwzB+sDgymZqaGkyYMAFJSUkYOnQo5s+fj48//tjqZNkWj8eD+++/H5dddhkyMzORkpKCiy++GKtXr8bx48eDrv/ss8+wcOFCDB8+HImJiRg/fjyqq6stSHl0cOLECYwZMwYOh0MyLAbnd2R0d3ejvLwcBQUFSExMRHp6Oi6//HJs3rw54DrO58g5duwYHnzwQVx44YUYNGgQMjMzccUVV+CFF14IupbzWxlr1qzBTTfd5GsjRo8eHfZ6tflqaH9KjGk89dRTBICuuOIKqqyspAcffJAyMjJo5MiRdODAAauTZ0t+8pOf0MCBA+kb3/gGPfHEE1RZWUk333wzAaDx48fTiRMnfNd2dXXRuHHjKCkpicrLy6mqqoquvfZaAkCrVq2y8C3sy49+9CMaNGgQAaDvfOc7Ab9xfkfGJ598QmPHjqW0tDS655576Nlnn6Unn3yS7rjjDlq7dq3vOs7nyDlz5gxdfvnl5HQ6aeHChbRu3Tp67LHH6JJLLiEAdN999/mu5fxWDgBKT0+n6dOnU1paGp177rkhr1Wbr0b3pyyOTOLw4cM0aNAgKioqolOnTvnONzU1kcPhoEWLFlmYOvvS1NREXV1dQefvvfdeAkBPP/2079zy5csJAG3cuDHg2jlz5lBCQgK1trYandyo4r333qO4uDh69NFHJcUR53dkTJ06lbKysuiTTz4Jex3nc+S89dZbBICWLl0acP7EiROUlZVFw4cP953j/FZOS0uL7/8LCwvDiiM1+WpGf8riyCTWr19PAGjDhg1Bv02dOpVSUlKor6/PgpRFJx9++GFQh52Tk0O5ublB1+7YsYMA0Jo1a8xMoq05ffo0TZgwgWbNmkX79u2TFEec39rZuXMnAaDHH3+ciIhOnTpFx44dk7yW8zlytm7dSgDokUceCfrt/PPPD8hfzm9tyIkjNflqRn/KPkcm0djYCAC4/PLLg367/PLLcfToUXz00UdmJytqOXDgAABg2LBhAIBDhw6hra0NU6ZMCbp2ypQpcDgcvm/EyLN27Vr8+9//xtNPPy35O+d3ZNTV1QEAxowZgxtuuAFJSUkYNGgQRo8eHZDnnM/6MHnyZAwePBiPPPIIfv/736OtrQ3/+c9/8MMf/hDNzc1YtWoVAM5vo1Cbr2b0pyyOTMLbWWdnZwf95j336aefmpqmaOXMmTN44IEHEB8fj1tuuQVA+Px3uVwYOnQo579CPv74Y6xcuRL33XcfcnNzJa/h/I4Mb8O+ePFiHDhwAOvXr8dvfvMbjBgxAnfeeScefPBBAJzPepGeno5XX30VqampuPnmmzFq1ChccMEF2LBhA7Zs2YLbbrsNAOe3UajNVzP6U9vurWY3Tpw4AQCSe8UkJiYGXMNExl133YVdu3bhoYceQkFBAYDw+Q989Q04/5Xxve99D+eeey5+/OMfh7yG8zsyjh49CgAYOHAgdu7c6cvHefPm4YILLsCaNWvwgx/8gPNZR9LS0nDJJZdg7ty5uPzyy3HkyBFUVlbi5ptvxsaNGzFz5kzOb4NQm69m9KdsOTKJ5ORkAEBfX1/QbydPngy4htHOT3/6UzzzzDNYvHgxVqxY4TsfLv+Br74B5788L730ErZu3YrKykokJCSEvI7zOzKSkpIAAGVlZQEdwIABA3DLLbfg5MmT2L17N+ezTvzzn//ElClTMH36dPz85z/H3Llzcfvtt+ONN97Aueeei4ULF6Kvr4/z2yDU5qsZ/SmLI5M455xzAEib+sKZCBnlrFq1CqtXr8Ztt92GdevWweFw+H4Ll/+9vb3o6Ojg/Jfhyy+/xA9/+EPMnj0bo0aNwv79+7F//35fnh49ehT79+9Hd3c353eEePNmxIgRQb95z3V2dnI+68Tjjz+O3t5e3HTTTQHnXS4Xrr/+ehw6dAgfffQR57dBqM1XM/pTFkcmMWnSJADAW2+9FfTbW2+9hUGDBuG8884zO1lRQ0VFBSoqKvDNb34Tzz33HJzOwKKdlZWF7OxsvP3220F/u2vXLhCR7xsx0pw4cQKff/45XnvtNeTm5vqOkpISAF9ZlXJzc1FZWcn5HSGXXXYZAKCtrS3ot08++QQAMHz4cM5nnfB2qKdOnQr6zXvu9OnTnN8GoTZfTelPI1rrxijmiy++oOTk5JBxGRYuXGhh6uxNRUUFAaBbbrmFTp8+HfK6ZcuWhYyjER8fHxCTgwnmyy+/pM2bNwcd69atIwDkdrtp8+bN5PF4iIjzOxK6urpo8ODBlJWVRUeOHPGd7+npoZEjR1JaWhodP36ciDif9WDp0qUEgH72s58FnO/p6aFRo0bRwIED6eTJk0TE+a0VuaX8avLVjP6UxZGJrF271hfR81e/+hU99NBDlJGRQVlZWfTpp59anTxb8vTTTxMAGjVqFG3YsIF++9vfBhzbtm3zXdvZ2Uljxoyh5ORkWrFiBVVXV9Ps2bODIuAy6ggV54jzOzK8sVzy8/PpkUceoZ///Od03nnnkcPhoOeff953Hedz5Ozfv5/S09PJ4XDQLbfcQpWVlfTwww/T2LFjCQA9+uijvms5v5Xzm9/8hh588EF68MEHadiwYTRkyBDfv5966qmAa9Xmq9H9KYsjk3nhhRfokksuocTEREpPT6d58+ZxRNUI+Na3vkUAQh5Tp04NuL69vZ0WLFhAmZmZ5HK5qLCwkCorK61JfJQQShwRcX5Hyh/+8Ae64ooraODAgZScnEwlJSVUV1cXdB3nc+S0tbXRHXfcQQUFBZSUlESDBg2i4uJievnll4Ou5fxWxtSpU0O2zVJWJLX5amR/6iAiimxijmEYhmEYJnpgh2yGYRiGYRg/WBwxDMMwDMP4weKIYRiGYRjGDxZHDMMwDMMwfrA4YhiGYRiG8YPFEcMwDMMwjB8sjhiGYRiGYfxgccQwDMMwDOMHiyOGYRiGYRg/WBwxTJSyYMECOBwO05+7YcMGOBwOvP7666Y/mzGeq666CqNHj7Y6GQxjKCyOGMZG9Pb24qmnnsLUqVORkZGBhIQEDBs2DG63G9XV1ejr6wv796+//jpWrVqFI0eORJSODz74AKtWrcL+/fsjug8TyFVXXQWHwyF5XHXVVVYnLySvvvoqVq1aZXUyGEY3eG81hrEJ+/fvx7XXXot///vfKC0thdvtRmZmJjo6OvD6669j69atWLRoEaqqqgAAp06dwpkzZ5CYmOi7x6pVq1BRUYF9+/ZFNPrfsGEDbr/9duzYsSOo0z5z5gxOnTqFAQMGwOnk8ZcarrrqKrz11lv49a9/HfTb8OHDcfXVV1uQqkCuuuoq7N+/P0AYL1iwAM8//zy4O2GihXirE8AwjDy9vb2YPXs2mpub8bvf/Q433XRTwO8//vGP8a9//Qvbtm3znUtISEBCQoLZSUVcXBzi4uJMf64dOH78OAYOHBj2GqfTiW9+85smpYhhGCl4WMcwNmD9+vX4v//3/+Kee+4JEkZeLrzwQtxzzz2+f/f3ObrqqqtQUVEBAMjNzfVN12zYsAEA8NFHH+GOO+5AYWEhUlJSkJycjAkTJqC6ujrgOQsWLMDtt98OAJg2bZrvPt5plVA+Rx0dHbjrrrswatQoDBgwACNHjsTixYtx8ODBgOtef/11X7qeffZZXHDBBXC5XDj33HPxyCOPBL33W2+9hVmzZiErKwsulwtZWVm4+uqr8cYbb8jmq9d/prW1Fddddx1SU1ORkpKC6667Dnv37g26nohQWVmJCRMmIDk5GSkpKZg2bRp27NgRcN3+/ft9eVJbW4sJEyYgKSkJ3//+92XTFI6zZ89i9erVuPLKK5GVlYUBAwZg1KhR+N73voeOjg7Jv9m4cSOmTZuGIUOGIDk5GQUFBbjrrrvw5ZdfAgjvI6bEv2j06NF4/vnnASBgGvD111/Hddddh4EDB6K7uzvo795//304HA7ce++96jKBYUyALUcMYwN+//vfAwC+853vaL7Hvffei/T0dGzevBmPP/44hg4dCgC4/PLLAXwlSt58801cf/31GDVqFI4dO4bf//73WLJkCQ4fPozy8nJfGlwuF6qqqrBixQqcf/75AIDx48eHfHZPTw+Ki4vR3NyMb33rW5g8eTL+9a9/Yd26ddi2bRuampowfPjwgL+prKzE559/jsWLFyM1NRUvvPACfvKTnyA7OxtlZWUAgObmZlx99dXIysrCXXfdhaysLHz++ed4++238f7776OkpEQ2X44fP45p06Zh8uTJWLNmDfbs2YNnnnkGu3fvxnvvvYeRI0f6rr311ltRU1ODG2+8Ebfffjv6+vrw4osv4uqrr8amTZvw9a9/PeDer776Kp566il873vfw3e/+10MHjxYNj0AcPjw4aBzaWlpOHXqFB599FHcdNNNmDt3LpKTk9HY2Ij169fjzTffxLvvvosBAwb4/ubee+/Fww8/jMLCQtxzzz3IyspCS0sLNm7ciAceeCDgWq2sXbsWjz32GN544w389re/9Z0///zzsWTJEvzhD39ATU0Nvvvd7wb83fr16+FwOLBo0aKI08AwukMMwwhPeno6paSkqPqbb33rW9S/iq9cuZIA0L59+4KuP378eNC5M2fO0NSpU2nw4MH05Zdf+s4/99xzBIB27NgR9DdSv917770EgNauXRtw7QsvvEAA6Nvf/rbv3I4dOwgAjRgxgrq6ugLSN3ToULrssst855544gkCQI2NjaGyISxTp04lAHT33XcHnN+0aRMBoMWLF/vObdy4kQDQr371q4BrT506RRMmTKDRo0fT2bNniYho3759BIASEhLoo48+Up0eqeP999+ns2fP0okTJ4L+7tlnnyUAVFtb6zu3e/duAkClpaXU29sbcP3Zs2d9aQ33LadOnUrnnnuu7Dmpskb0VfkZNWoUTZgwIeD8yZMnKS0tjUpLS8NlB8NYBk+rMYwN6OnpUWx10EpycrLv/3t7e9HR0YHOzk5cc8016OnpwUcffaT53ps3b0Z6ejruuOOOgPNlZWUYN24cNm/eHPQ3t99+O4YMGRKQvssuuwx79uzxnfP+/uqrr6K3t1dz+pYvXx7w77lz56KgoACvvvqq79yLL76IgQMH4vrrr8fhw4d9x5EjRzBnzhzs378/IG0AcO2116KgoEBVWhISEvDnP/856Bg3bhwcDgeSkpIAfOX4fuTIERw+fBilpaUAgN27dwekFwBWr14Nl8sV8Azv1JfROJ1OLFy4EO+++y4+/PBD3/lNmzahq6sLixcvNjwNDKMFFkcMYwMGDx6Mo0ePGvqMY8eO4cc//jFGjRqFpKQkDB06FJmZmT6fkK6uLs33bm1tRX5+fpCDuMPhQGFhIQ4fPoyenp6A38aMGRN0n4yMjADfmm984xtwu914+OGHkZaWhmnTpmHNmjXYt2+f4rQNGTIEWVlZQefPP/98HD582Ocv85///AfHjx9HVlYWMjMzAw6vv9Vnn30WcI+8vDzF6fDidDoxffr0oGPQoEEAgN/97ne49NJLkZSUhLS0NGRmZvryyv8beYVauOlOM1i8eDHi4uKwfv1637n169cjPT0dN9xwg4UpY5jQsM8Rw9iAiy66CH/729/Q0tKCsWPHGvKM+fPn409/+hOWLFmCK6+8Eunp6YiPj0ddXR0ef/xxnD171pDnUojl30pWvA0YMAD19fV455130NDQgJ07d6KiogIVFRV47rnnMH/+fNl7hLKgeNPl/Z2IkJ6ejtra2pD3uvDCCwP+7W+N04ONGzdi3rx5mDx5Mp544gnk5OQgMTERZ86cwYwZMwK+EREpsg6Fu+b06dMRp/mcc87BrFmz8MILL+CRRx7BwYMHsWPHDtx5551BFi2GEQUWRwxjA2688Ub87W9/Q3V1NX72s59pvk+ojvDIkSP405/+hFtvvRW/+tWvAn7bvn274vuEYsyYMfB4PDh16lSQ9ejf//43hg4dGtG04cSJEzFx4kTce++9OHjwICZMmIDly5crEkddXV04dOhQkPXoo48+CkhXfn4+mpubMWnSJKSmpmpOayS88MILSExMxI4dOwKEl9SUZ0FBAerr6/Hhhx9iypQpIe+Znp4OAOjs7Az6bd++fYrCQciVhyVLluCPf/wjNm/ejH//+98gIp5SY4SGp9UYxgYsWrQI559/Pn7xi19g06ZNktf885//xC9+8Yuw9/FOzfSfIvNaafpbcQ4ePIhnn31W8X1CMXfuXHR2dmLdunUB519++WXs3btX8/SK1KquESNGYMSIEZKdfSj6C87NmzejubkZ119/ve/crbfeCiJCeXm5pLWr/5SaEcTFxcHhcARZiB566KGga70r+n76059KRk73vkN+fj6AYBFcU1OD9vZ2RemSKw+zZs1CTk4OqqqqsGHDBkyePBkXXXSRonszjBWw5YhhbEBSUhJee+01XHvttfg//+f/YPr06bjmmmswdOhQdHR04G9/+xvq6urw7W9/O+x9Lr30UgBAeXk55s+fD5fLhUsvvRS5ubm45ppr8MILLyApKQmTJk3Cxx9/jHXr1iE3Nzcohs7EiRPhdDqxZs0adHV1ITk5GRdeeGHQtJKXZcuW4ZVXXsFdd92F999/H5MmTfIt5c/OzsYDDzygKV8eeughbNu2DbNnz0Zubi4AYOvWrXjvvfcUxxQaOnQoNm3ahPb2dlx11VW+pfzDhw/3xYUC4Fu+X1lZiQ8++ABz5szB0KFD8emnn+Ltt9/G3r170draquk9lHLjjTdi48aNKC0txW233YZTp07h1VdfxYkTJ4KunTx5Mn7yk5/gf/7nfzBhwgTMmzcPWVlZ2LdvH1555RU0NjZiyJAhKCgowPTp07Fu3ToQES6++GJ88MEH2Lx5M8aNG4dTp07JpuvSSy/F008/je9///uYOXMmEhISUFpaimHDhgH4yo9q0aJFPt+s+++/X9d8YRjdsWiVHMMwGjh+/DitXbuWiouLKS0tjeLj4ykzM5PcbjetX7+e+vr6fNeGWl69evVqGjVqFMXFxREAeu6554iI6IsvvqBFixbRiBEjyOVy0YUXXkhVVVUhl3qvX7+e8vPzKT4+ngDQypUriSj00vDDhw/TD37wA8rOzqaEhATKysqiRYsW0YEDBwKu8y7l96bLn/7vtGPHDrr55pvp3HPPpcTERBoyZAhNnDiRnnnmGTp9+rRsfnqXpbe0tNDXv/51SklJoUGDBtHXv/512rNnj+Tf/OY3v6Hi4mJKSUkhl8tF5557Ls2dO5defvll3zXepfzePFHK1KlTyeVyhb2mqqqKzj//fHK5XJSVlUXf/va3qaOjgwDQt771raDrX3rpJbr88stp0KBBlJycTAUFBXT33XcHlJWDBw/SjTfeSCkpKTRw4ECaMWMG/fvf/1a8lP/06dO0dOlSysrKIqfTKfn929raKC4ujgYOHEg9PT1qsoVhTIf3VmMYJmaR2ieMMYZDhw4hJycHt912W8DKNYYREfY5YhiGYQznmWeewenTpyOK8s4wZsE+RwzDMIxhvPzyy/jkk0/w85//HNdccw0mT55sdZIYRhYWRwzDMIxhzJ8/H4mJiSgpKcGvf/1rq5PDMIpgnyOGYRiGYRg/2OeIYRiGYRjGDxZHDMMwDMMwfrA4YhiGYRiG8YPFEcMwDMMwjB8sjhiGYRiGYfxgccQwDMMwDOMHiyOGYRiGYRg/WBwxDMMwDMP48f8APE+lxAc30mwAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "96abb04b",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<div><img src=\"attachment:q8.jpg\" style=\"height: 300px;\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b7897b3",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:08.504086Z",
+     "iopub.status.busy": "2023-12-06T02:04:08.504086Z",
+     "iopub.status.idle": "2023-12-06T02:04:08.769975Z",
+     "shell.execute_reply": "2023-12-06T02:04:08.768964Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot using the DataFrame 'citations_overall' with the x-axis labelled \"Citations per Faculty\" \n",
+    "# and the y-axis labelled \"Overall\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4b64b888",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 9:** Create a **scatter plot** representing the `Academic Reputation` (on the **x-axis**) against the `Employer Reputation` (on the **y-axis**) of each institution from the *United States* in the `year` *2023*.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `reputations_usa` containing the **Academic Reputation**, and the **Employer Reputation** data from the `Year` *2023*, of each **institution** in the `Country` *United States*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0205db15",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:08.774980Z",
+     "iopub.status.busy": "2023-12-06T02:04:08.774980Z",
+     "iopub.status.idle": "2023-12-06T02:04:08.789591Z",
+     "shell.execute_reply": "2023-12-06T02:04:08.788575Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'reputations_usa', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c0b23e5",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q9\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7396270e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `reputations_usa` as **scatter plot** with the **x-axis** labelled *Academic Reputation* and the **y-axis** labelled *Employer Reputation*.\n",
+    "\n",
+    "You **must** use the `scatter_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `public_tests.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q9.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "1a338332",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<div><img src=\"attachment:q9.jpg\" style=\"height: 300px;\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75c882ba",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:09.984833Z",
+     "iopub.status.busy": "2023-12-06T02:04:09.983833Z",
+     "iopub.status.idle": "2023-12-06T02:04:10.748651Z",
+     "shell.execute_reply": "2023-12-06T02:04:10.747640Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot using the DataFrame 'reputations_usa' with the x-axis labelled \"Academic Reputation\" \n",
+    "# and the y-axis labelled \"Employer Reputation\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "402d0e41",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 10:** Create a **scatter plot** representing the `International Students` (on the **x-axis**) against the `Faculty Student` (on the **y-axis**) for the **top ranked** institution of **each** `Country` in the `Year` *2023*.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** `top_ranked_inter_faculty` containing the **International Students**, and the **Faculty Student** data from the `Year` *2023*, of the **top** ranked **institution** (i.e., the institution with the **least** `rank`) of each **country**.\n",
+    "\n",
+    "**Hint:** You can use the `MIN` SQL function to return the least value of a selected column. However, there are a few things to keep in mind while using this function.\n",
+    "* The function must be in **uppercase** (i.e., you must use `MIN`, and **not** `min`).\n",
+    "* The column you are finding the minimum of must be inside backticks (``` ` ```). For example, if you want to find the minimum `Rank`, you need to say ```MIN(`Rank`)```.\n",
+    "\n",
+    "If you do not follow the syntax above, your code will likely fail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0785ff5",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:10.752670Z",
+     "iopub.status.busy": "2023-12-06T02:04:10.752670Z",
+     "iopub.status.idle": "2023-12-06T02:04:10.768913Z",
+     "shell.execute_reply": "2023-12-06T02:04:10.767883Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'top_ranked_inter_faculty', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ef0cdee",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q10\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8726dfb",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `top_ranked_inter_faculty` as **scatter plot** with the **x-axis** labelled *International Students* and the **y-axis** labelled *Faculty Student*.\n",
+    "\n",
+    "You **must** use the `scatter_plot` function to create the plot.\n",
+    "\n",
+    "**Important Warning:** `public_tests.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q10.jpg": {
+     "image/jpeg": 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TTt2lS5eiTZs2eOihh2wSc2u1ts/d1OcnJiZi6NCh/MAhIofGzyqyBqdtQcrOzsb69euRk5ODAwcOYMCAAdBoNFizZg2OHz+O1157Db6+vgCAmTNn4tNPP8XYsWOxf/9+7Ura27Ztw+zZsxEXF2fnn8Y4jX3uSqVSp5vM09MTaWlpTX443LjCrDnPJyIickvWX7fSesrLy8XUqVNFUlKS8PPzE4GBgWLAgAHio48+Mqh78uRJMWHCBBEeHi58fHxEjx49xKpVq8x6X3uupK1Wq41aOba5FWaPHDnClWeJiMgtmXL/lgghhJ1yM6dVXV2N4OBgVFVV2W1G28363OVyebMtReyzJyIid2TK/ZsJkhkcIUFqiUqlQlJSUovHmRQREZG7MeX+7bSDtKl5XGGWiBxRa3cAILIlJkguiCvMEpEjae0OAET2wATJBXGFWSJyJK3dAYDIHpgguSiuMEtEjqC1OwAQ2YvTroNELeMKs0TkCFq7AwCRvTBBcnGJiYn88CEiu+GYSHJW7GIjIiKr4ZhIMoYjznBkgkRERFbFMZHUHEee4ciFIs3g6AtFEhE5Io6JJH032/XB0riStpUxQSIiImode+z6wJW0iYiIyKE5+q4PTJCIiIjI5hx9hiMTJCIiIrI5R5/hyASJiIiI7MKRZzhyoUgiIiKyC0fe9YEJEhEREdmVI+76wC42IiIiIj1MkIiIiIj0MEEiIiIi0mNWguTp6YkPP/yw2eMff/yxwbQ9IiIiImdhVoJ0s91JuHsJEREROTOzZ7FJJJJmjx0+fBjBwcHmvjQREbkRlUqF0tJSh5riTWR0grRu3TqsW7dO+/+XXnoJq1evNqin0Whw4MABjBgxwjIREhGRS1Kr1cjMzERRUZG2TCaTQaFQICQkxI6REZmQIJ0/fx5lZWUArrcenT17FrW1tTp1JBIJAgMDMXHiRLz00kuWjZRaxG9gRORsMjMzoVQqdcqUSiUyMjJQWFhop6iIrpMIMwYMeXh4YMOGDcjMzLRGTA6vuroawcHBqKqqQlBQkF1j4TcwInJGKpUKSUlJLR7nlz2yNFPu32YN0i4rK8PIkSPNeSpZWEvfwIiIHFVpaWmLx0tKSmwUCVmaSqVCQUEBiouL7R1Kq5iVIMXExMDf39/SsZCJVCoVioqK0NDQoFPe0NCAoqIip//jJCLXFR8f3+LxhIQEG0VClqJWqyGXy5GUlIT09HRIpVLI5XJoNBp7h2YWsxeK3LVrF8aMGYM+ffogPj4ecXFxOo+b/fFT6/EbGBE5K6lUCplMZrBmnqenJ2QyGbvXnJCr9WiYNc1//fr1mDhxIry8vCCVStGlSxdLx0VG4DcwInJmCoUCGRkZOmMo09LSoFAo7BgVmaOxR0PfjT0azpb0mpUgLVy4EElJSVAqlejUqZOlYyIjNX4DUyqVOt1snp6eSEtLc7o/RiJnxpmkpgsJCUFhYSGKi4tRUlLCc+fEjOnRcLbfrVldbMeOHcOUKVOYHDkAhUKBtLQ0nTJ+AyOyHVcbd2EPiYmJGDp0qNPdQOl/XLFHw6wEqXPnzrhy5YqlYyETNM4SqKysRGFhIVQqFfLz86FSqVBYWMgp/kQ24mrjLojM4YpjysxKkCZNmoQPPvjAYPYUWV9z31bDwsL4DYzIxjiTlOh/XK1Hw6wxSH369MHmzZvRp08fPPnkk4iNjTXIGgHg7rvvbnWApIsrzxI5Dlccd0FkLlcbU2b2Sto6L6K3ca0QAhKJxGVbmOy1krY5K89y4CiR9RhzTQoheA0SOQhT7t9mtSCtXbvWrMCodUz5tsotSIisr6WZpHfffTeeeuopXoNETsqsFiR35wwtSHK5vNnp/+yKI7IcjUZjsJaPTCbD1atXsWPHDl6DRA7ElPs3EyQz2HOzWmMSH24CSWR7N467EELwGiRyQFbfrBYAysvLkZWVhaioKHh7e+Prr78GAJw9exZZWVn44YcfzH1paoExswS4BQmR7d24lg+vQSLnZ9YYpLKyMvTt2xeXL19G3759cerUKe2x8PBw7Nu3D++++y569+5tsUDpOmNmCbjigl1EzoTXIJHzM6sF6fnnn4enpyd+++03fPDBB9DvpUtPT8d3331nkQCpaS2tPOuKC3YRORNeg0TOz6wESalU4oknnkB0dLTBFH8AiImJwYkTJ1odHJnP1RbsInI2vAaJnJtZXWzV1dXo2LFjs8evXLmC+vp6s4Oi1nO1BbuInA2vQSLnZlaCFB0djd9//73Z47t27WIfu4NITEzkhzKRHfEaJHJOZnWxjRo1Cu+99x5+++03bVljV9vHH3+MTz/9FKNHj7ZMhEREREQ2ZtY6SNXV1UhJScHRo0fRv39/fPXVVxg8eDCqqqrw448/olevXvjvf/8LX19fa8Rsd/ZcB4mIiIjMY/V1kIKCgrBr1y48+uij+OmnnyCEwNdff43S0lJMnToV27dvd9nkiIiIiFyfRVbSPnv2LIQQCA8Pb3JWm6thCxIREZHzsfpmtfrCw8Mt8TKkR6VScRdwIiIiOzAqQdq5c6dZL3733Xeb9Tx3p1arkZmZyV3Aichi+IWLyDRGdbF5eHiY1XV244aqrsTaXWzGbEhLRGQMfuEi+h+Ld7GtXbvWoOyNN97AoUOHMGbMGHTv3h0A8Pvvv+PDDz/ELbfcgmnTppkROqlUKp0PskYNDQ0oKipCcXExv/0RkdEyMzOhVCp1ypRKJTIyMviFi6gFRiVI48eP1/n/ihUrcOrUKRw8eBDR0dE6x55//nmkpKTgwoULlovSjRizCzgTJCIyBr9wEZnPrGn+b7zxBiZPnmyQHAHX92GbMmUK3njjjVYH5464CzgRWYoxX7iILEWlUqGgoADFxcX2DsUizEqQjh07hnbt2jV7vF27djh27Ji5Mbk17gJORJbCL1xkC2q1GnK5HElJSUhPT4dUKoVcLodGo7F3aK1iVoIUFRWFTz75pMlB2PX19fj444/RuXPnVgdnjKqqKuTk5CApKQm+vr4IDQ1Fv379sHnzZp16Z86cQVZWFiIiIuDr64vk5GSsXr3aJjGairuAE5El8AsX2UJL49ycmVnrIE2ePBnPPfcc/vrXv+Kf//wnunXrBolEgj/++ANLlizB7t27sXjxYkvHaqC8vByDBw+GWq3GxIkT0b17d9TW1uLQoUM4fvy4tt758+cxYMAAVFRUIDs7G7Gxsdi6dSsmTZqEkydPYs6cOVaP1RTcBZyILEWhUCAjI0NnLBK/cJGluPQ4N2Gm7Oxs4eHh0eRj+vTp5r6sSVJTU0VkZKQ4fvx4i/VmzpwpAIiNGzfqlA8fPlx4eXmJI0eOmPS+VVVVAoCoqqoyOWYiIntQqVQiPz9fqFQqe4dCLiQ/P18AaPaRn59v7xB1mHL/btVWIyqVCp999pl2IGB8fDxGjBgBqVTayrTt5r799lvcfffdWLZsGbKzs1FfX4+6ujoEBAQY1O3SpQvatGmDI0eO6JR/8803GDx4MHJzczFz5kyj35tbjRAREV3PA5KSklo87kgtSDbbakQqlWLGjBmteQmz5efnAwDi4uIwatQo/Oc//0F9fT1iYmIwY8YM7TpMp0+fRnl5OTIzMw1eIyUlBRKJBHv37rVp7ERERK6gcZxbc4sbO1JyZCqzBmk7gkOHDgEAHnvsMVRUVGDNmjVYv349OnbsiKeeegoLFiwAAFRUVAC4PrBcn4+PD8LCwnDixIkW36uurg7V1dU6DyIiInLdiUVmtSANGTLkpnUkEgm++uorc17eKDU1NQCAgIAA7Ny5Ez4+PgCAhx56CN27d0dubi6mTZuG2tpaANAe1+fr66ut05zc3FzMmzfPgtETERG5BledWGRWgnTkyBGDvdnq6+tx6tQpXLt2DWFhYU2OBbIkPz8/ANenF96Y/Hh7e2PMmDGYP38+9uzZg/DwcADXW4GacunSJURGRrb4Xjk5OZg+fbr2/9XV1U0ukklEROSuEhMTXSIxamRWgnT06NEmy+vq6rB06VKsXbsWO3bsaE1cN9XYZdaxY0eDY41larUavXr1AoAmu9EuX76Mc+fOYeDAgS2+l4+PT7MtUEREROR6LDoGycfHBzk5Objrrrt0WlysoW/fvgCur4Wkr3ENpIiICERGRiIqKgq7du0yqLd7924IIdC7d2+rxkpERETOxSqDtAcMGNDkwlGWdP/99yMoKAjr169HVVWVtrympgbr1q1DSEgIUlJSAFzvhisrK8OmTZt0XmPp0qVo06YNHnroIavGSkRERM6lVdP8m1NWVoYrV65Y46W12rVrh2XLluHRRx9Fnz598Nhjj0EikWDNmjU4deoU8vLy4O/vDwCYOXMmPv30U4wdOxb79+/XrqS9bds2zJ49G3FxcVaNlYiIiJyLWQnSjdt43EitVkOpVOLf//43Bg0a1Jq4jJKVlYXw8HC88sormDdvHoQQuOOOO7B06VIMHTpUWy8kJATfffcdZs2ahdWrV6O6uhoJCQlYtWoVpkyZYvU4iYiIyLmYtZK2h4eHwSy2RkIIdOvWDZ999pnL7hTNlbSJiIicj9VX0n7xxRcNEiSJRILQ0FBIpVKkpaXBw8Np16AkIiIiN9eqvdjclS1akFQqFUpLS11mwS0iIiJ7M+X+bVYzT1ZWFvbs2dPs8b179yIrK8ucl3Z7arUacrkcSUlJSE9Ph1QqhVwuh0ajsXdoREREbsOsBCkvLw+lpaXNHi8rK8O6devMDsqdZWZmQqlU6pQplUpkZGTYKSIiIiL3Y5WBQtXV1fD29rbGS7s0lUqFoqIinR2RAaChoQFFRUUoLi62U2RERETuxehB2r/++it+/vln7f+//fZb1NfXG9TTaDRYuXIlunXrZpEA3UlLrXIAUFJSwvFIRERENmB0grR582btjvYSiQRvv/023n777SbrBgYGQqFQWCZCNxIfH9/icVddNoGIiMjRGJ0gTZgwAYMGDYIQAkOGDMHzzz+PtLQ0nToSiQSBgYHo3r07fH19LR6sq5NKpZDJZFAqlTrdbJ6enkhLS2PrERERkY0YnSDFxMQgJiYGADBnzhz87W9/Q8+ePa0WmLtSKBTIyMjQ2csuLS2NLXJEREQ2xHWQzGCLdZCKi4tRUlLCdZCIiIgsxCrrIKnVavz444+ora3VKRdC4JVXXoFUKkVAQAD69OljME2dTJeYmIihQ4cyOSIiIrIDoxOkV155BQMGDDCYgp6Tk4NZs2ahtLQUfn5+2LdvH4YNG4ZffvnF4sESERER2YLRCdJ3332H9PR0tG3bVltWVVWF119/HeHh4Th48CAqKyuxY8cOeHl5YenSpVYJmIiIiMjajE6QysrKcNttt+mUffXVV6irq8MzzzwDqVQKABg4cCAyMzPx7bffWjZSIiIiIhsxaQxSp06ddMr27NkDiUQCmUymU96rVy+cOnXKMhESERER2ZjRCVJ4eDjOnDmjU7Z79254e3ujR48eOuXe3t7w8vKyTIRERERENmZ0gtS9e3d88sknuHbtGgDg1KlT2LNnD/r27WuQDJWVlSEiIsKykRIRERHZiNELRf7jH//AsGHDMHDgQPTv3x/btm3D1atX8eijjxrU/fLLL5GcnGzRQImIiIhsxegEKT09HTNnzsSrr76KXbt2AQCeeuopPPLIIzr1fv75Z/zwww+YOHGiZSMlIiIishGTV9KurKxEaWkp4uLiEB4ebnD8zJkzOHHiBLp164aAgACLBepIbLGSNhEREVmWKfdvo1uQGoWFhSEsLKzZ4xERERx/RERERE7N6EHaRERERO6CCRIRERGRHiZIRERERHqYIBERERHpYYJEREREpMesBKmmpsbScRARERE5DLMSpI4dOyIrKwvff/+9peMhIiIisjuzEqQhQ4Zgw4YNGDhwILp3746lS5eisrLS0rERERER2YVZCdJnn32G8vJyLFq0CNeuXcOMGTMQFRWF0aNH44svvrB0jEREREQ2ZfJWI0359ttvsWbNGnz66ae4dOkSoqOjkZWVhYkTJyI6OtoScToUbjVCRETkfEy5f1skQWpUU1ODJ598Ehs2bIBEIoGHhweGDh2K559/HnfddZel3sbumCARERE5H1Pu3xaZ5n/27Fm89tpr6NOnDzZs2IC2bdvi8ccfx+TJk7Fjxw70798fq1evtsRbEREREVmd2S1IQggUFBRgzZo12LZtG65evYo77rgDkyZNQmZmJgICAgAAVVVV+Nvf/gaVSoXjx49bNHh7YQsSERGR8zHl/t3GnDeYPXs28vLycPLkSQQGBiIrKwuTJ09Gr169DOoGBwdj/PjxmDBhgjlv5VZUKhVKS0uRkJCAxMREe4dDRETktsxKkBYuXIg777wTc+bMQUZGhra1qDm33347XnzxRbMCdAdqtRqZmZkoKirSlslkMigUCoSEhNgxMiIiIvdkVhfbzz//3GRrkbuwdBebXC6HUqlEQ0ODtszT0xNpaWkoLCxs9esTERGRDQZpT58+HV999VWzx7dv344hQ4aY89JuR6VSoaioSCc5AoCGhgYUFRWhuLjYTpERERG5L7MSpG+++QZnzpxp9viff/6JHTt2mB2UOyktLW3xeElJiY0iISIiokYWmeav7+zZs/D19bXGS7uc+Pj4Fo8nJCTYKBIiIiJqZPQg7Z07d+Kbb77R/n/Tpk1Ntm5oNBp89NFHuPXWWy0SoKuTSqWQyWTNjkHibDYiIiLbM3qQ9rx58zBv3rzrT5JI0NLT4uPjsWHDBpdaPftGlh6krdFokJGRwVlsREREVmSVrUaqqqpw/vx5CCEQFxeH5cuX4/7779d9MYkEgYGBCA0NNT96J2CthSKLi4tRUlLCdZCIiIiswCoLRQYHByM4OBgAsHbtWqSmpiImJqZ1kZKOxMREJkZEREQOwKyFIsePH2/pOIiIiIgchlEJ0vr168168XHjxpn1PCIiIiJ7MmoMkoeHx00HZhu8sERisPihq+BmtURERM7H4mOQtm/fbpHAyDzcxJaIiMi2jEqQUlNTrR0HNYGb2BIREdmHVVbSJsvIzMyEUqnUKVMqlcjIyLBTRERERO7BrFlsxg7a5iBt8zVuYqvvxk1s2d1GRERkHWYlSBMmTGhy0LZEItH5PxMk8xmziS0TJCIiIuswK0FqatB2fX09SktLsXLlSvj7+2PhwoWtDs6dcRNbIiIi+zF6qxFj1dXVoU+fPhg3bhz++c9/WvKlHYatpvnL5fJmN7EtLCy02vsSERG5IlPu3xYfpO3j44NHHnkEK1eutPRLux2FQoG0tDSdsrS0NCgUCjtFROTeVCoVCgoKUFxcbO9QiMjKzOpiuxkfHx9UVFRY46XdSkhICAoLC7mJLZGdcckNIvdj8S62U6dO4a9//SuEEDh48KAlX9phONpK2lxIksi62N1N5BosvpK2viFDhjRZrlarcejQIVy5cgXr1q0z56Vbpba2Fj179kRZWRkmT56Mt956S+f4mTNnkJOTg88//xxVVVWQSqV46qmn8Pjjj9s8Vkvgt1oi6+OSG0TuyawE6ciRIwZT+iUSCUJDQzFq1ChMmzYN/fr1s0iApnjxxRdx9uzZJo+dP38eAwYMQEVFBbKzsxEbG4utW7di0qRJOHnyJObMmWPjaFuvpYUk+a2WyDK45AaRezIrQTp69KiFw2i9n376CcuXL8crr7yCGTNmGBx/5ZVXUFJSgo0bN2LUqFEAgMcffxwjRozAwoULMW7cOMTGxto6bLPxWy2RbXDJDSL35BJbjTQ0NODxxx+HTCbD3/72tybrfPDBB4iNjdUmR42mT5+Oq1ev4uOPP7ZFqBZjzLdaImo9qVQKmUwGT09PnXJPT0/IZDJ+ESFyUWYlSEqlEjk5Oc0ez8nJaXIxSWtZvnw5/vjjD6xYsaLJ46dPn0Z5eTlSUlIMjqWkpEAikWDv3r3Nvn5dXR2qq6t1HvbGb7VEtsMlN4jcj1kJ0uLFi1tsoSgrK8Mrr7xidlCmOHbsGObMmYPZs2c320XWuORAVFSUwTEfHx+EhYXhxIkTzb5Hbm4ugoODtY/o6GjLBN8K/FZLZDuNS26oVCrk5+dDpVKhsLCQkyGIXJhZCdIvv/yCvn37Nnv8rrvuwi+//GJ2UKZ44oknEBMT0+S4o0a1tbUAridDTfH19dXWaUpOTg6qqqq0j/Ly8tYFbSH8VktkW4mJiRg6dCi/gBC5AbMGaVdVVSEgIKDZ435+ftBoNGYHZawPP/wQBQUF2LFjB7y8vJqt5+/vD+B6V1lTLl26hMjIyGaf7+Pj02xyZU+2WEiSaywREZE7MitB6ty5M3788cdmj+/fv7/FhMMSrly5gmeeeQbDhg1Dly5dtDPrGrvKampqcPToUYSEhKBz5846x250+fJlnDt3DgMHDrRqvNaUmJho8eSFaywREZElOOsXbbO62O677z7k5eXh66+/NjimVCqxbt06pKentzq4ltTW1uLPP//Etm3bEBsbq300JjoffvghYmNjsWrVKkRGRiIqKgq7du0yeJ3du3dDCIHevXtbNV5n09IaS0RERDejVqshl8uRlJSE9PR0SKVSyOVym/QwWYJZW42cOXMGt99+O06fPg25XI7k5GQAwM8//4wvvvgCkZGR2LdvHzp27GjxgBtdvXoVn3/+uUH5n3/+icmTJ0Mmk2HKlCno0aMHEhMT8dxzz2Hx4sU66yABwIgRI1BQUIDDhw8jLi7OqPd2tK1GLE2lUiEpKanF4870LYCIiGzPEbfosfpWIxEREfj+++/xxBNPoLCwEAUFBQCur6Y9dOhQrFixwqrJEQB4eXlh5MiRBuWNXW1du3bVOT5z5kx8+umnGDt2LPbv369dSXvbtm2YPXu20cmRO+DKwURE1BqusJixWQkSAMTExCA/Px8ajQYlJSUQQiAxMdFhx6eEhITgu+++w6xZs7B69WpUV1cjISEBq1atwpQpU+wdnkPhGktERNQarvBF26wuNnfn6l1sgGM2jRIRkXNw1KEapty/W73VyIULF3DixAkcP37c4EHOi2ssERGRuVxhMWOzW5A++ugjvPTSSzh48GCzdW5sfXAl7tCC1MiaaywREZHr0mg0yMjIcKjlYqw+SHvLli3IzMyEVCrF5MmT8dZbbyEzMxP19fXYsmUL/vKXv2DYsGFmBU+OxRprLBERkeuzxWLG1mRWC9KAAQOg0Wiwf/9+XLhwAR06dIBSqcSQIUPw22+/oX///li/fj3uv/9+a8Rsd+7UgkREROQqrD4G6ddff8X48ePh6+sLD4/rL9HYndazZ09MmjQJubm55rw0ERERkd2ZlSA1NDSgffv2AK7vuwZc35+tUVJSEn777TcLhEdERERke2YlSFFRUdp9zfz8/NChQwfs27dPe/zw4cMtbmZLplOpVCgoKEBxcbG9QyEiInJ5Zg3S7tevH7788kvMmTMHwPXtOl5//XX4+/vj2rVrePPNNzF8+HCLBuquuGksERGR7Zk1SPuHH37A5s2bMXv2bPj5+eHs2bNIS0vDgQMHAAA9evRAfn4+oqOjLR6wI7DlIG0u2EhERGQZpty/jU6Qdu7ciVtuuQXh4eFNHhdC4MCBA/D09MQtt9yiHbztimyVIDnqSqRERETOyCqz2AYPHowvv/xS+/8LFy5g1KhR2sHYEokEycnJ6NGjh0snR7ZkzF42REREZHlGZzL6DU11dXXYsmUL/vzzT4sHRddx01gyFQfzExFZBpt6HJgr7GVDtqFWqyGXy5GUlIT09HRIpVLI5XJoNBp7h0ZE5JSYIDk4bhpLxsjMzIRSqdQpUyqVyMjIsFNERETOzaxp/mQ7zr6XDVmfSqXSWQaiUUNDA4qKilBcXMy/GSIiE5mUIG3btk27QGRtbS0kEgk++ugjnUUiG0kkEjz77LOWiZK4aSw1y5jB/PzbISIyjdHT/E2dmSaRSHTW7nEl3KyWHAmXgyAiMo4p92+jW5C2b9/e6sCIyPIaB/M3t6AokyMiItMZnSClpqZaMw4iagWFQoGMjAydsUgczE9EZD4O0iazqFQqlJaWctC4g+BgfiIiy2KCRCbh5rmOjYP5iYgsg+sgkUm43g4REbkDJkhktMb1dvRnJ9643g4REZErYIJERuPmuURE5C6YIJHRuHkuERG5CyZIZDRunktERO6CCRKZhJvnEhGRO+A0fzIJ19shIiJ3wASJzML1doiIyJWxi42IiIhIDxMkIiIiIj3sYnMw3OOMiIjI/tiC5CDUajXkcjmSkpKQnp4OqVQKuVwOjUZj79CIiIjcDhMkB8E9zoiIiBwHEyQHwD3OiIicm0qlQkFBAT+vXQgTJAfAPc6IiJwTh0e4LiZIDoB7nBEROScOj3BdTJAcAPc4IyJyPhwe4dqYIDkI7nFGRORc3HF4hDuNteI6SA6Ce5wRETkXdxoeoVarkZmZiaKiIm2ZTCaDQqFASEiIHSOzHrYgOZjExEQMHTqUyRERkYNzp+ER7jjWigkSERGRmdxheIS7jrViFxsREZGZ3GF4hDFjrVztZwaYIBEREbVaYmKiwyYJrd3j053GWt2IXWxEREQuyFKLWLrTWKsbMUEiIiJyQZYcWO0OY630SYQQwt5BOJvq6moEBwejqqoKQUFB9g6HiIhIh0qlQlJSUovHzWn5cfaxVqbcvzkGiYiIyMVYa2C1I4+1sjR2sREREbkYdx1YbUlMkIiIiFyMuw6stiQmSE7MnfbEISIi01hiYLU732c4BskJueOeOEREZJrWLGLJ+wxnsZnF3rPY5HI5lEqlzrLvnp6eSEtLQ2Fhoc3jISIi1+Kq9xlT7t9MkMxgzwTJWlM3iYiIANe+z5hy/+YYJCdjzNRNIiIic/E+cx0TJCfDqZtERGRNvM9cxwTJyXDqJhERWRPvM9c5bYKkUqnw4osvom/fvggPD0fbtm3Rq1cvLFy4EBcvXjSof+bMGWRlZSEiIgK+vr5ITk7G6tWr7RB567njnjhERGQ7vM848SDtmTNnYsWKFRg+fDhSUlLg7e2N7du34//+7/+QnJyM3bt3w8/PDwBw/vx59O7dGxUVFcjOzkZsbCy2bt2Kzz//HHPnzsWcOXNMem97z2Jr5Ox74hARkWNztfuMW8xi27dvHxISEtCuXTud8hdeeAELFy7EihUr8OSTTwIAcnJy8PLLL2Pjxo0YNWqUtu6IESNQWFiIw4cPIzY21uj3dpQEiYiIiIznFrPY7rzzToPkCABGjx4NADhw4IC27IMPPkBsbKxOcgQA06dPx9WrV/Hxxx9bNVZzuPPqpURERPbmtAlScyoqKgAAHTp0AACcPn0a5eXlSElJMaibkpICiUSCvXv3tviadXV1qK6u1nlYi1qthlwuR1JSEtLT0yGVSiGXy6HRaKz2nkRERKTLpRKkhoYGzJ8/H23atMGYMWMA/C9hioqKMqjv4+ODsLAwnDhxosXXzc3NRXBwsPYRHR1t+eD/v8zMTCiVSp0ypVKJjIwMq70nERER6XKpBOnpp5/G7t27MXfuXO0qoLW1tQCuJ0NN8fX11dZpTk5ODqqqqrSP8vJyywb+/6lUKhQVFeks7Q5cT/yKiorY3UZERGQjLpMgvfDCC1i5ciUee+wxzJo1S1vu7+8P4Ho3WVMuXbqkrdMcHx8fBAUF6TysgauXEhEROQaXSJDmzp2LhQsXYty4cXj77bchkUi0xzp37gwATXajXb58GefOnWuy+80euHopERGRY3D6BGnevHmYN28eHnnkEaxduxYeHro/UmRkJKKiorBr1y6D5+7evRtCCPTu3dtW4baIq5cSERE5BqdOkObPn4+5c+dizJgxyMvLM0iOGmVmZqKsrAybNm3SKV+6dCnatGmDhx56yBbhGqW51UsXLFjAaf9EREQ24rQLRb755puYNm0aunTpgvnz5xu0ukREROCee+4BAGg0Gtx55504ffq0zkra27Ztw+zZszF//nyT3tsWC0U2rl4aFhaG2bNno6ioSHtMJpNBoVAgJCTEKu9NRETkitxiJe0JEyZg3bp1zR5PTU3FN998o/3/qVOnMGvWLHz++eeorq5GQkICpk2bhilTppj83rZcSVsul0OpVOrMbPP09ERaWhoKCwut+t5ERESuxC0SJHuyVYKkUqm0yxU0d5zjkoiIiIzjFluNuANO+yciInfhaFtsMUFyYJz2T0RErs5Rt9higuTAOO2fAMf7VkVEZEmOusUWEyQH19y0f4VCYaeIyFYc9VsVEZGlOPIWW0yQHFxISAgKCwuhUqmQn58PlUqFwsJCTvF3A476rYqIyFIceaxtG7u9M5kkMTGRXWpupPFblb4bv1Xx74GInJ0jj7VlCxKRA3Lkb1VERJbiyGNtmSAROSBH/lZFRGRJjjrWll1sRA6o8VtVc6uos3uNiFxF41jbxi22EhISHOIzji1IRA7KUb9VERFZQ2JiIoYOHeoQyRHAFiQih+Wo36qIiNwBEyQiB8cZjEREtscuNiIiIiI9bEEit6ZSqVBaWsruKyIi0sEWJHJL3MaDiIhawgSJ3BK38SAiopawi43cRmN3mqenJ7fxICKiFjFBIpenVquRmZnZZFLUlJKSEiZIRERujl1s5PKa6k5rCbfxICIitiCRS1OpVEa3HHEbDyIiasQWJHJppaWlRtflNh5ERNSILUjk0uLj41s8/sUXX6C+vp7rIBERkQ4mSOTSpFIpZDIZlEolGhoatOWN3Wn33HOPHaMjIiJHxS42cnkKhQJpaWk6ZexOIyKilrAFiVxeSEgICgsLUVxcjJKSEnanERHRTTFBIreRmJjIxIiIiIzCLjYiIiIiPUyQiIiIiPQwQSIiIiLSwwSJiIiISA8TJCIiIiI9nMXmQFQqFUpLSzkNndwerwUisje2IDkAtVoNuVyOpKQkpKenQyqVQi6XQ6PR2Ds0IpvitUBEjoIJkgPIzMyEUqnUKVMqlcjIyLBTRET2wWuBiByFRAgh7B2Es6murkZwcDCqqqoQFBTUqtdSqVRISkpq8Ti7GMgd8FogImsz5f7NFiQ7Ky0tbfF4SUmJjSIhsi9eC0TkSJgg2Vl8fHyLxxMSEmwUCZF98VogIkfCBMnOpFIpZDIZPD09dco9PT0hk8nYpUBug9cCETkSJkgOQKFQIC0tTacsLS0NCoXCThER2QevBSJyFBykbQZLDtK+UXFxMUpKSmy69gvXmyFHZI9rwVp4jRE5DlPu30yQzGCtBMmW1Go1MjMzUVRUpC2TyWRQKBQICQmxY2SujTdL98FrjMjxcBYb3RTXm7EtLoDofniNETk3tiCZwdlbkLjejO3J5XIolUo0NDRoyzw9PZGWlobCwkI7RkbWwGuMyDGxBYlaxPVmbEulUqGoqEgnOQKAhoYGFBUVobi42E6RkbXwGiNyfkyQ3BDXm7Et3izdD68xIufHBMkNcb0Z2+LN0v3wGiNyfkyQ3BTXm7Ed3izdE68xIufGQdpmcPZB2jdypfVmHJlGo0FGRganfLshXmNEjoPrIFmZKyVIZFu8WRIR2Y8p9+82NoqJiAAkJiYyMSIicgIcg0RERESkhwkSERERkR4mSERERER6mCARERER6WGCRERERKSHCRIRERGRHiZIRERERHqYIBERERHpYYJEREREpIcJEhEREZEebjVihsbt66qrq+0cCRERERmr8b5tzDa0TJDMUFNTAwCIjo62cyRERERkqpqaGgQHB7dYRyKMSaNIx7Vr13Dy5Em0bdsWEonE5OdXV1cjOjoa5eXlN91NmCyD59y2eL5tj+fc9njObcsS51sIgZqaGnTq1AkeHi2PMmILkhk8PDwQFRXV6tcJCgriRWVjPOe2xfNtezzntsdzblutPd83azlqxEHaRERERHqYIBERERHpYYJkBz4+PpgzZw58fHzsHYrb4Dm3LZ5v2+M5tz2ec9uy9fnmIG0iIiIiPWxBIiIiItLDBImIiIhIDxMkIiIiIj1MkIiIiIj0MEGyMYVCgTvuuAN+fn4ICwtDRkYGjh07Zu+wnJpKpcKLL76Ivn37Ijw8HG3btkWvXr2wcOFCXLx40aD+mTNnkJWVhYiICPj6+iI5ORmrV6+2Q+Suo7a2FnFxcZBIJJgyZYrBcZ5zy6iqqkJOTg6SkpLg6+uL0NBQ9OvXD5s3b9apx/PdehcuXMCCBQvQs2dPBAYGIjw8HP3798eGDRsM6vJ8myY3NxcPPvig9jOja9euLdY39fxa7D4ryGbeeOMNAUD0799frFq1SixYsEC0b99edOrUSVRUVNg7PKf13HPPiYCAAPHwww+L119/XaxatUqMHj1aABDJycmitrZWW1ej0YiEhATh5+cncnJyxDvvvCPuu+8+AUDMnTvXjj+Fc/vnP/8pAgMDBQAxefJknWM855Zx/PhxER8fL0JCQsT06dPFu+++K/7973+LqVOniuXLl2vr8Xy3XkNDg+jXr5/w8PAQWVlZ4u233xZLly4Vt912mwAgZs+era3L8206ACI0NFSkpaWJkJAQERMT02xdU8+vJe+zTJBspLKyUgQGBorbb79dXL16VVv+ww8/CIlEIh599FE7RufcfvjhB6HRaAzKn3/+eQFArFixQls2c+ZMAUBs3LhRp+7w4cOFl5eXOHLkiLXDdTk//vij8PT0FK+99lqTCRLPuWWkpqaKyMhIcfz48Rbr8Xy33vfffy8AiOzsbJ3y2tpaERkZKSIiIrRlPN+mKy0t1f67R48eLSZIppxfS99nmSDZyJo1awQAkZeXZ3AsNTVVtG3bVtTV1dkhMtf1yy+/GNywo6OjRWxsrEHd7du3CwAiNzfXliE6vfr6enHHHXeI9PR0UVZW1mSCxHPeejt37hQAxLJly4QQQly9elVcuHChybo8361XUFAgAIjFixcbHLvlllt0zi/Pd+vcLEEy5fxa+j7LMUg2snfvXgBAv379DI7169cPNTU1OHTokK3DcmkVFRUAgA4dOgAATp8+jfLycqSkpBjUTUlJgUQi0f6eyDjLly/HH3/8gRUrVjR5nOfcMvLz8wEAcXFxGDVqFPz8/BAYGIiuXbvqnHueb8vo06cPgoKCsHjxYnzyyScoLy/HwYMH8cwzz+Dw4cOYO3cuAJ5vazP1/Fr6PssEyUYab9ZRUVEGxxrLTpw4YdOYXFlDQwPmz5+PNm3aYMyYMQBa/h34+PggLCyMvwMTHDt2DHPmzMHs2bMRGxvbZB2ec8to/FB/7LHHUFFRgTVr1mD9+vXo2LEjnnrqKSxYsAAAz7elhIaGYsuWLQgODsbo0aPRpUsXdO/eHXl5edi6dSvGjRsHgOfb2kw9v5a+z7YxKVoyW21tLQA0uYeMr6+vTh1qvaeffhq7d+/GSy+9hKSkJAAt/w6A678H/g6M98QTTyAmJgYzZsxotg7PuWXU1NQAAAICArBz507t+XzooYfQvXt35ObmYtq0aTzfFhQSEoLbbrsNDzzwAPr164fz589j1apVGD16NDZu3IihQ4fyfFuZqefX0vdZtiDZiL+/PwCgrq7O4NilS5d06lDrvPDCC1i5ciUee+wxzJo1S1ve0u8AuP574O/AOB9++CEKCgqwatUqeHl5NVuP59wy/Pz8AACZmZk6H/7e3t4YM2YMLl26hD179vB8W8iBAweQkpKCtLQ0vPrqq3jggQcwceJEfPvtt4iJiUFWVhbq6up4vq3M1PNr6fssEyQb6dy5M4Cmm/daahYk08ydOxcLFy7EuHHj8Pbbb0MikWiPtfQ7uHz5Ms6dO8ffgRGuXLmCZ555BsOGDUOXLl1w9OhRHD16VHtea2pqcPToUVRVVfGcW0jjOerYsaPBscYytVrN820hy5Ytw+XLl/Hggw/qlPv4+GDkyJE4ffo0Dh06xPNtZaaeX0vfZ5kg2Ujv3r0BAN9//73Bse+//x6BgYHo1q2brcNyKfPmzcO8efPwyCOPYO3atfDw0P3zjoyMRFRUFHbt2mXw3N27d0MIof09UfNqa2vx559/Ytu2bYiNjdU+Bg4cCOB661JsbCxWrVrFc24hffv2BQCUl5cbHDt+/DgAICIigufbQhpvplevXjU41lhWX1/P821lpp5fi99njZ7vRq1y9uxZ4e/v3+z6DFlZWXaMzvnNmzdPABBjxowR9fX1zdb717/+1eyaGm3atNFZn4OaduXKFbF582aDx9tvvy0ACJlMJjZv3ixUKpUQgufcEjQajQgKChKRkZHi/Pnz2vLq6mrRqVMnERISIi5evCiE4Pm2hOzsbAFAvPzyyzrl1dXVokuXLiIgIEBcunRJCMHz3Vo3m+Zvyvm19H2WCZINLV++XLvC51tvvSVeeukl0b59exEZGSlOnDhh7/Cc1ooVKwQA0aVLF5GXlyfef/99nccXX3yhratWq0VcXJzw9/cXs2bNEqtXrxbDhg0zWB2XTNfcOkg855bRuMaLVCoVixcvFq+++qro1q2bkEgkYt26ddp6PN+td/ToUREaGiokEokYM2aMWLVqlVi0aJGIj48XAMRrr72mrcvzbbr169eLBQsWiAULFogOHTqIdu3aaf//xhtv6NQ19fxa8j7LBMnGNmzYIG677Tbh6+srQkNDxUMPPcSVVltp/PjxAkCzj9TUVJ36J0+eFBMmTBDh4eHCx8dH9OjRQ6xatco+wbuQ5hIkIXjOLeWzzz4T/fv3FwEBAcLf318MHDhQ5OfnG9Tj+W698vJyMXXqVJGUlCT8/PxEYGCgGDBggPjoo48M6vJ8myY1NbXZz+umWpNMPb+Wus9KhBDC+A45IiIiItfHQdpEREREepggEREREelhgkRERESkhwkSERERkR4mSERERER6mCARERER6WGCRERERKSHCRIRERGRHiZIRERERHqYIBGRw+vatSsGDRpk7zAMHD16FBKJBHPnzrV3KGZx1PNK5AiYIBE5gG+++QYSiQQvv/yy2a+xZcsWp71RA8Dy5cuRl5dn7zCs6s8//8S//vUv9OzZE23btkVwcDASExPx8MMPY9OmTTp18/LysHz5cvsEagU///wz5s6di6NHj9o7FCKjtLF3AERkGVu2bMG6deucNklavnw5unbtigkTJhgcO3z4MCQSie2DsqDy8nL07t0bNTU1GDNmDJ544gkAQElJCT7//HNcuHABo0aN0tbPy8vD0aNHkZ2dbaeILevnn3/GvHnzMGjQIHTt2tXe4RDdFBMkIrqpuro6eHp6ok0b+3xk+Pj42OV9LenVV1/FmTNn8Nlnn2H48OE6x5YtW4YTJ07YKTIiagq72Igc1I3jW7Zs2YI77rgDvr6+6NixI5599lnU19dr63bt2hXr1q0DAEgkEu3jm2++0dYpLi7G2LFj0bFjR3h7e6Nr16549tlncfHiRZ33nTBhAiQSCc6ePYusrCxERETAz88PJ06cwNy5cyGRSHDo0CH861//QufOneHj44Nbb70V+fn5Bj/DypUrce+996Jz587w9vZGx44d8cgjj+h0szT+nMeOHcOOHTt04r/x52tqrMx//vMfDBw4EG3btkVAQAD69OkDhUJhUK+x1eLEiRMYPXo0QkJCEBAQAJlMBpVKpVO3pqYGL7zwAu666y6EhYXBx8cHCQkJmDlzJmpra1v8nbWk8X0GDx7c5PGoqCjtvyUSCXbs2IFjx47pnI/G8yaRSJpsacvLyzP4vQPAwYMHcd999yEwMBDt2rXD/fffjyNHjjQbq1KpxL333ot27drB19cXycnJeOuttwzqNf5efv/9d8jlcm234d///necPn1aW2/ChAmYOHGi9udv/HkaWzvVajWmT5+O+Ph4+Pr6IiQkBMnJyVi4cGGzMRJZG1uQiBxcfn4+Vq5ciSlTpuCxxx7D1q1b8dprryEkJASzZs0CcL17aunSpfj222/x/vvva597yy23AAD279+PIUOGoF27dpg8eTI6d+6MX3/9Ff/+97/x3//+Fzt27ICXl5fO+95zzz3o1KkTZs+ejYsXLyIwMFB7bPz48fDx8cGzzz6LK1euYPny5Rg5ciRUKpVO98mSJUvQr18/3HPPPWjXrh1+++03vPvuu/j6669x4MABtG/fHuHh4Xj//ffxzDPPICwsDM8//7xR5+Wdd97B5MmTkZiYiJycHHh7e2PDhg3IzMxEWVmZ9tw0unjxIlJTU5GSkoJFixahrKwMr7/+Ou6//3789ttv8PT0BABUVFRgzZo1ePDBBzFmzBh4enpix44dWLx4MX766ScUFRUZ/8u7QVxcHABg9erVyM7ObrHL8P3338fChQtRWVmJZcuWacvDw8NNft+ysjIMGDAAtbW1mDp1KuLi4vDVV19h8ODBTSZ877zzDqZMmYK+ffvi+eefR2BgIL788ks88cQTKC0txauvvqpTv6KiAkOGDMGoUaPwwAMP4KeffsI777yD6upqfPHFFwCAyZMnw8fHB++88w5mzZql/btMTk4GADz44IPYuXMnJk+ejFtvvRWXLl2CSqXCN998Y/TfA5HFCSKyu+3btwsAIjc3V1tWVlYmAAh/f39RVlamLb927Zro0aOHiIyM1HmN8ePHi+Yu6eTkZCGVSkV1dbVO+aZNmwQAsXbtWoPXGTdunMHrzJkzRwAQ9913n7h27Zq2fO/evQKAmDlzpk79CxcuGLyGUqkUAMQrr7yiUx4TEyNSU1ObjF//mEajEQEBAaJr167i/Pnz2vKLFy+K5ORk0aZNG3Hs2DFteWpqapPvuXjxYgFAFBYWasvq6urE1atXDWJ44YUXBACxZ88ebVnj72jOnDlNxn2j0tJSERQUJACI6OhokZmZKZYtWyb27dvXZP3U1FQRExPT5DEAYvz48Qbla9euFQDE9u3btWUZGRkCgCgoKNCp++STTwoAOuf15MmTwsfHRzz88MMGr/30008LDw8PUVJSoi2LiYkRAMTHH3+sU3fq1KkCgDh48GCLsQkhxPnz5wUAMXXq1CZ/ViJ7YRcbkYMbOXKkTquMRCLB4MGDcfr0aVy4cOGmzz9w4AB+/fVXPPzww6irq0NlZaX2MWDAAAQEBGi/6d9o+vTpzb7mP/7xD50WkN69e6Nt27YoLi7WqRcQEAAAuHbtGqqqqlBZWYlbb70VwcHB2LNnz01jb86XX36Jixcv4qmnnkJwcLC23N/fHzNmzEB9fT0+++wzned4eHjg6aef1ikbMmQIAOjE7e3trR1rVV9fD41Gg8rKSqSlpQGA2XHHxcXhl19+wdSpU3Ht2jV8+OGHeOaZZ3DnnXciOTkZ+/fvN+t1W3Lt2jX85z//wa233gq5XK5zTL+FDQA+/fRT1NXVYeLEiTp/J5WVlRg+fDiuXbuGr776Suc5nTp1wujRo3XKGs9rSUnJTWP08/ODr68vdu/ezRlu5FCYIBE5uMaumRu1b98eAHDu3LmbPv/gwYMAgPnz5yM8PFzn0aFDB1y8eBFnzpwxeF5iYqJJMYWGhhrE8/XXX2PQoEEICAhAu3bttO9bVVUFjUZz09ib0zh+pkePHgbH/vKXv+jUadSpUyf4+vrqlDV3HleuXInk5GT4+PggNDQU4eHh2jFQrYm7a9euePPNN3HixAmcPHkSGzduxIgRI3DgwAEMGzYMarXa7Nduyp9//okLFy5ou7Ru1KlTJ53kEvjf34pMJjP4W7nnnnsAwOBvpbV/n97e3nj99dfx+++/IzY2Ft27d8e0adPw5ZdfGvdDElkJxyARObjGsTFNEULc9PmNdbKzs3Hfffc1WSckJMSgzN/f3+SYboxn7969uPfee5GQkICXX34ZsbGx8PPzg0QiwcMPP4xr167dNPbmtPRzN3fM2PO4ZMkSzJgxA/feey+efvppdOrUCd7e3qioqMCECRNaFfeNOnbsiFGjRmHUqFHIzMyEQqFAfn4+HnnkEbNf88aB+zcydomExvOwdu1anUHjN9JPiFr79wkAkyZNwogRI/D5559j586d2Lx5M958802MHDkSGzduhIcHv8uT7TFBInIRzd0EpVIpgOtdTI3dRLagUCjQ0NCAgoICxMbGassvXrzYZCuMKescxcfHAwB+//13yGQynWO///67Th1TbdiwAV27dkVBQYHOjbmwsNCs1zNGSkoKFAoFKioqtGUtnY/Q0NAmW5v0W806dOiAwMBA/PHHHwZ1T548iaqqKp2yxr+V9u3bW/xv5Wa/38jISDz66KN49NFHce3aNTz++ON47733sGPHjmZn/hFZE9NyIhfROMtMP/no1asX/vKXv+Cdd95pckxIfX29xbt2gP+1LOi3IixatKjJVpjAwECju6/uueceBAQEYMWKFaiurtaWX758GUuWLEGbNm0M1hoyJW6JRKITd319fatWOQeA7du349KlSwbljeOEAKB79+7a8sDAQJw/f77JVhipVIpdu3bpzELTaDRYu3atTj0PDw+MGDECv/zyi0GCt2jRIoPXffDBB+Hj44O5c+c2OcOtqqoKdXV1N/lJm9bc32dtba3Be3l4eKBXr14AYJW/TSJjsAWJyEXcddddWLFiBZ588kkMHToUXl5eGDJkCDp06ID169djyJAh6NWrF7KystCjRw/U1taipKQEmzZtQm5ubpPr6rTGAw88gGXLliE9PR2TJk2Ct7c3vvzyS/z6668ICwtrMv733nsPc+fORVJSkrYrrint2rXDkiVLMGXKFPTu3RsTJ06El5cXNmzYgJ9//hkLFy5Ely5dzIr773//O3JycjB06FCMGjUK1dXV+PDDDw2WQTDVkiVL8N///hfDhg3DHXfcgeDgYJw+fRobN27E/v37MXjwYJ0u0Lvuugvbtm3D008/jb59+8LT0xPDhw9HQEAApk2bhkceeQRDhgzB2LFjcf78eaxevRoxMTE66w8BwEsvvYTCwkI88MADePLJJ7XT/Pft22fwe4iKisKqVavw2GOP4ZZbbsG4ceMQExODs2fP4sCBA9iyZQv++OMPs1bCvvPOO+Hh4YHc3FxoNBr4+/ujZ8+eqK+vR2pqKh544AH06NED7du3x6FDh7Bq1Sp06tTJpq2eRDrsNHuOiG7Q0jT/pqaQN063v3H6f319vcjOzhaRkZHCw8PDYEr10aNHxeTJk0VMTIzw8vISoaGh4vbbbxczZ84Ux48f19ZrabmApt63UVPT9Ddv3ixuv/124e/vL9q3by8eeughcezYsSbrnjp1Stx///0iODhYANCJobklALZu3Sr69+8v/P39hZ+fn+jdu7f44IMPDOo1N2W+qXNcX18vFi1aJOLj44W3t7fo0qWLePbZZ8Uff/xhUNeUaf67du0S06dPF3feeafo0KGDaNOmjQgODhZ9+/YVS5YsEZcvX9apX1NTI8aNGyfat28vJBKJwXlfvHix6NKli/D29hbdunUTa9asaXYq/R9//CHS09NFQECACAoKEiNGjBClpaXNntfvvvtOjBw5UoSHhwsvLy/RsWNHMWjQIPHaa6+JS5cuaes19/zGv+cbl48QQog1a9YIqVQq2rRpoz1vlZWVIjs7W9x6662iXbt2wtfXV8TFxYmpU6fq/F0S2ZpECCNH0RERERG5CY5BIiIiItLDBImIiIhIDxMkIiIiIj1MkIiIiIj0MEEiIiIi0sMEiYiIiEgPEyQiIiIiPUyQiIiIiPQwQSIiIiLSwwSJiIiISA8TJCIiIiI9TJCIiIiI9Pw/lZfF8MZnHeMAAAAASUVORK5CYII="
+    }
+   },
+   "cell_type": "markdown",
+   "id": "55c65d85",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<div><img src=\"attachment:q10.jpg\" style=\"height: 300px;\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5b7e9ca",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:11.807627Z",
+     "iopub.status.busy": "2023-12-06T02:04:11.806641Z",
+     "iopub.status.idle": "2023-12-06T02:04:12.063658Z",
+     "shell.execute_reply": "2023-12-06T02:04:12.062640Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot using the DataFrame 'top_ranked_inter_faculty' with the x-axis labelled \"International Students\" \n",
+    "# and the y-axis labelled \"Faculty Student\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1e07968",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Correlations:\n",
+    "\n",
+    "You can use the `.corr()` method on a **DataFrame** that has **two** columns to get the *correlation* between those two columns.\n",
+    "\n",
+    "For example, if we have a **DataFrame** `df` with the two columns `Citations per Faculty` and `Overall`, `df.corr()` would return\n",
+    "\n",
+    "||**Citations per Faculty**|**Overall**|\n",
+    "|---------|------|---------|\n",
+    "|Citations per Faculty|1.000000|0.617044|\n",
+    "|Overall|0.617044|1.000000|\n",
+    "\n",
+    "You can use `.loc` here to **extract** the *correlation* between the two columns (`0.617044` in this case)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d473b7f9",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 11:** Find the **correlation** between `International Students` and `Overall` for institutions from the `Country` *United Kingdom* that were ranked in the **top** *100* in the `year` *2022*.\n",
+    "\n",
+    "Your output **must** be a **float** representing the absolute correlation. The **only** `pandas` operations you are **allowed** to use are: `.corr`, `.loc` and `.iloc`. You **must** use SQL to gather all other data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "935ba9ca",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:12.068653Z",
+     "iopub.status.busy": "2023-12-06T02:04:12.068653Z",
+     "iopub.status.idle": "2023-12-06T02:04:12.084470Z",
+     "shell.execute_reply": "2023-12-06T02:04:12.082457Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'uk_inter_score_corr', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c64060e",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q11\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7649cf84",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Let us now define a new score called `Citations per International` as follows:\n",
+    "\n",
+    "$$\\texttt{Citations per International} = \\frac{\\texttt{Citations per Faculty} \\times \\texttt{International Faculty}}{100}.$$\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2d81f69",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 12:** Find the **correlation** between `Citations per International` and `Overall` for **all** institutions in the `year` *2024*.\n",
+    "\n",
+    "Your output **must** be a **float** representing the absolute correlation. The **only** `pandas` operations you are **allowed** to use are: `.corr`, `.loc` and `.iloc`. You **must** use SQL to gather all other data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01855a94",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:12.569395Z",
+     "iopub.status.busy": "2023-12-06T02:04:12.569395Z",
+     "iopub.status.idle": "2023-12-06T02:04:12.581179Z",
+     "shell.execute_reply": "2023-12-06T02:04:12.580169Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'cit_per_inter_score_corr', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ef4f690",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q12\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb05ad59",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 13:** What are the **top** *15* countries with the **highest** *total* of `Citations per International` in the `Year` *2024*.\n",
+    "\n",
+    "The *total* `Citations per International` of a `Country` is defined as the **sum** of `Citations per International` scores of **all** institutions in that `Country`. You **must** display the columns `Country` and `Sum of International Citations`. The rows **must** be in *descending* order of `Sum of International Citations`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** that looks like this:\n",
+    "\n",
+    "||**Country**|**Sum of International Citations**|\n",
+    "|---|---|---|\n",
+    "|**0**|United States|2294.2671|\n",
+    "|**1**|United Kingdom|2279.9530|\n",
+    "|**2**|Australia|1895.6595|\n",
+    "|**3**|Canada|822.9573|\n",
+    "|**4**|Netherlands|749.9450|\n",
+    "|**5**|Switzerland|664.2349|\n",
+    "|**6**|Germany|635.0223|\n",
+    "|**7**|China (Mainland)|578.7473|\n",
+    "|**8**|Hong Kong SAR|513.1582|\n",
+    "|**9**|France|385.9691|\n",
+    "|**10**|Sweden|382.8463|\n",
+    "|**11**|New Zealand|344.3393|\n",
+    "|**12**|Belgium|300.6716|\n",
+    "|**13**|Denmark|217.8851|\n",
+    "|**14**|Finland|210.7134|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7117c50",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:13.021679Z",
+     "iopub.status.busy": "2023-12-06T02:04:13.020678Z",
+     "iopub.status.idle": "2023-12-06T02:04:13.035842Z",
+     "shell.execute_reply": "2023-12-06T02:04:13.035842Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'top_cit_per_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59ab8ce4",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q13\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57aec620",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 14:** Among the institutions ranked within the **top** *300*  in the `Year` *2023*, find the **average** `Citations per International` for **each** `Country`.\n",
+    "\n",
+    "You **must** display the columns `Country` and `Average Citations per International` representing the **average** of `Citations per International` for **each** `Country`. The rows **must** be in *descending* order of `Average Citations per International`.\n",
+    "\n",
+    "**Hint:** To find the **average**, you can use `SUM()` and `COUNT()` or you can simply use `AVG()`.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** whose **first ten rows** look like this:\n",
+    "\n",
+    "||**Country**|**Average Citations per International**|\n",
+    "|---|---|---|\n",
+    "|**0**|Singapore|92.950000|\n",
+    "|**1**|Australia|82.001726|\n",
+    "|**2**|Hong Kong SAR|78.318000|\n",
+    "|**3**|Switzerland|78.004875|\n",
+    "|**4**|Netherlands|58.039117|\n",
+    "|**5**|United Kingdom|56.838479|\n",
+    "|**6**|Sweden|52.991567|\n",
+    "|**7**|Canada|48.342191|\n",
+    "|**8**|Denmark|47.686267|\n",
+    "|**9**|Belgium|47.580433|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12f99017",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:13.539810Z",
+     "iopub.status.busy": "2023-12-06T02:04:13.539810Z",
+     "iopub.status.idle": "2023-12-06T02:04:13.555455Z",
+     "shell.execute_reply": "2023-12-06T02:04:13.554444Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'avg_cit_per_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd23b46b",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q14\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a651fd5d",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 15** Find the **institution** with the **highest** value of `Citations per International` for **each** `Country` in the `Year` *2024*.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** with the columns `Country`, `Institution Name`, and a new column `Maximum Citations per International` representing the **maximum** value of `Citations per International` for that country. The rows **must** be in *descending* order of `Maximum Citations per International`. You **must** **omit** rows where `Maximum Citations per International` is **missing** by using the clause:\n",
+    "\n",
+    "```sql\n",
+    "HAVING `Maximum Citations per International` IS NOT NULL\n",
+    "```\n",
+    "\n",
+    "**Hint:** You can use the `MAX()` function to return the largest value within a group.\n",
+    "\n",
+    "Your output **must** be a **DataFrame** whose **first ten rows** look like this:\n",
+    "\n",
+    "||**Country**|**Institution Name**|**Maximum Citations per International**|\n",
+    "|---|---|---|---|\n",
+    "|**0**|United States|Massachusetts Institute of Technology (MIT) |100.0000|\n",
+    "|**1**|Hong Kong SAR|City University of Hong Kong|99.9000|\n",
+    "|**2**|Switzerland|University of Bern|99.2000|\n",
+    "|**3**|Australia|The University of Western Australia|98.9000|\n",
+    "|**4**|Canada|Western University|98.0051|\n",
+    "|**5**|Macau SAR|University of Macau|96.9000|\n",
+    "|**6**|China (Mainland)|Zhejiang University|95.3552|\n",
+    "|**7**|Singapore|Nanyang Technological University, Singapore (NTU)|94.4000|\n",
+    "|**8**|United Kingdom|Imperial College London|94.0000|\n",
+    "|**9**|France|Institut Polytechnique de Paris|92.3930|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55dc6c25",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:13.992131Z",
+     "iopub.status.busy": "2023-12-06T02:04:13.992131Z",
+     "iopub.status.idle": "2023-12-06T02:04:14.014310Z",
+     "shell.execute_reply": "2023-12-06T02:04:14.013299Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'max_cit_per_inter', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5840a0e",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q15\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbbdfd94",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 16**: Among the institutions ranked within the **top** *50*  in the `Year` *2022*, create a **horizontal bar plot** representing the **average** of both the`Citations per Faculty` and `International Faculty` scores for **all** institutions in **each** `Country`.\n",
+    "\n",
+    "You **must** first create a **DataFrame** `country_citations_inter` with **three** columns: `Country`, `Average Citations per Faculty` and `Average International Faculty` representing the name, the average value of `Citations per Faculty` and the average value of `International Faculty` for each country respectively.\n",
+    "\n",
+    "You **must** ensure that the countries in the **DataFrame** are **ordered** in **increasing** order of the **difference** between the `Average Citations per Faculty` and `Average International Faculty`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2745f090",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:14.430798Z",
+     "iopub.status.busy": "2023-12-06T02:04:14.430798Z",
+     "iopub.status.idle": "2023-12-06T02:04:14.452264Z",
+     "shell.execute_reply": "2023-12-06T02:04:14.451254Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'country_citations_inter', then display it\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4963ed8f",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q16\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68d7d67b",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `country_citations_inter` as **horizontal bar plot** with the **x-axis** labelled *Country*.\n",
+    "\n",
+    "You **must** use the `horizontal_bar_plot` function to plot this data. Verify that the countries are **ordered** in **decreasing** order of the **difference** between `Average Citations per Faculty` and `Average International Faculty`. Verify that the **legend** appears on your plot.\n",
+    "\n",
+    "**Hint:** If you want the countries in the plot to be ordered in **decreasing** order of the difference, you will need to make sure that in the DataFrame, they are ordered in the **increasing** order.\n",
+    "\n",
+    "**Important Warning:** `public_tests.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q16.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "a52f1633",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<div><img src=\"attachment:q16.jpg\" style=\"height: 300px;\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03b34bac",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:15.249612Z",
+     "iopub.status.busy": "2023-12-06T02:04:15.249612Z",
+     "iopub.status.idle": "2023-12-06T02:04:15.635174Z",
+     "shell.execute_reply": "2023-12-06T02:04:15.634163Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the horizontal bar plot using the DataFrame 'country_citations_inter' with the x-axis labelled \"Country\" \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "058ff1ef",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 17:** Create a **scatter plot** representing the `Overall` (on the **x-axis**) against the `Rank` (on the **y-axis**) for **all** institutions in the `Year` *2022*. Additionally, **plot** a **regression line** within the same plot.\n",
+    "\n",
+    "You **must** first compute a **DataFrame** containing the **Overall**, and the **Rank** data from the `Year` *2022*. You **must** use the `get_regression_line` function to compute the best fit line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "554f5b22",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:15.639193Z",
+     "iopub.status.busy": "2023-12-06T02:04:15.639193Z",
+     "iopub.status.idle": "2023-12-06T02:04:15.657191Z",
+     "shell.execute_reply": "2023-12-06T02:04:15.656161Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'overall_rank', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "222b0f7d",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q17\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c87dbebc",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `overall_rank` as **scatter plot** with a **regression line** with the **x-axis** labelled *Overall* and the **y-axis** labelled *Rank*.\n",
+    "\n",
+    "You **must** use the `regression_line_plot` function to plot this data.\n",
+    "\n",
+    "**Important Warning:** `public_tests.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q17.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "5fef6ac8",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<div><img src=\"attachment:q17.jpg\" style=\"height: 300px;\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c5f6090",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:16.865229Z",
+     "iopub.status.busy": "2023-12-06T02:04:16.864229Z",
+     "iopub.status.idle": "2023-12-06T02:04:17.195715Z",
+     "shell.execute_reply": "2023-12-06T02:04:17.194705Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot and the regression line using the DataFrame 'overall_rank' with the x-axis labelled \"Overall\" \n",
+    "# and the y-axis labelled \"Rank\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad06f06e",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Does our linear regression model fit the points well? It looks like the relationship between the `Overall` and `Rank` is **not quite linear**. In fact, a cursory look at the data suggests that the relationship is in fact, inverse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c3c0102",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:17.200717Z",
+     "iopub.status.busy": "2023-12-06T02:04:17.199734Z",
+     "iopub.status.idle": "2023-12-06T02:04:17.204991Z",
+     "shell.execute_reply": "2023-12-06T02:04:17.203961Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Food for thought is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f766d24",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 18:** Create a **scatter plot** representing the **inverse** of the `Overall` (on the **x-axis**) against the `Rank` (on the **y-axis**) for **all** institutions in the `Year` *2022*. Additionally, **plot** a **regression line**  within the same plot.\n",
+    "\n",
+    "The `Inverse Overall` for each institution is simply defined as `1/Overall` for that institution. You **must** first compute a **DataFrame** containing the **Inverse Overall**, and the **Rank** data from the `Year` *2022*. You **must** use the `get_regression_line` function to compute the best fit line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e5568c5",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:17.208991Z",
+     "iopub.status.busy": "2023-12-06T02:04:17.208991Z",
+     "iopub.status.idle": "2023-12-06T02:04:17.226270Z",
+     "shell.execute_reply": "2023-12-06T02:04:17.225257Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first compute and store the DataFrame 'inverse_overall_rank', then display its head\n",
+    "# do NOT plot just yet\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1edf4354",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q18\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b26a070c",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `inverse_overall_rank` as **scatter plot** with a **regression line** with the **x-axis** labelled *Inverse Overall* and the **y-axis** labelled *Rank*.\n",
+    "\n",
+    "You **must** use the `regression_line_plot` function to plot this data.\n",
+    "\n",
+    "**Important Warning:** `public_tests.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q18.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "c03f9c32",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<div><img src=\"attachment:q18.jpg\" style=\"height: 300px;\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5364ddef",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:18.287969Z",
+     "iopub.status.busy": "2023-12-06T02:04:18.287969Z",
+     "iopub.status.idle": "2023-12-06T02:04:18.631761Z",
+     "shell.execute_reply": "2023-12-06T02:04:18.630750Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the scatter plot and the regression line using the DataFrame 'inverse_overall_rank'\n",
+    "# with the x-axis labelled \"Inverse Overall\" and the y-axis labelled \"Rank\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4fb9c82",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "This seems to be much better! Let us now use this **regression line** to **estimate** the `Rank` of an institution given its `Overall`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52a2bc98",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 19:** Use the regression line to **estimate** the `Rank` of an institution with an `Overall` of *72*.\n",
+    "\n",
+    "Your output **must** be an **int**. If your **estimate** is a **float**, *round it up* using `math.ceil`.\n",
+    "\n",
+    "\n",
+    "**Hints:**\n",
+    "1. Call the `get_regression_coeff` function to get the coefficients `m` and `b`.\n",
+    "2. Recall that the equation of a line is `y = m * x + b`. What are `x` and `y` here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93dcf736",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:18.636781Z",
+     "iopub.status.busy": "2023-12-06T02:04:18.635766Z",
+     "iopub.status.idle": "2023-12-06T02:04:18.644521Z",
+     "shell.execute_reply": "2023-12-06T02:04:18.644521Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# compute and store the answer in the variable 'rank_score_72', then display it\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3f2fc1f",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q19\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fe92766",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** Can you find out the `Overall` of the university with this `Rank` in the `Year` *2022*? Does it match your prediction?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6aa6864",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:19.032462Z",
+     "iopub.status.busy": "2023-12-06T02:04:19.032462Z",
+     "iopub.status.idle": "2023-12-06T02:04:19.037548Z",
+     "shell.execute_reply": "2023-12-06T02:04:19.036516Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Food for thought is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "34b82ce3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Question 20:** Using the data from Question 5, create a **pie plot** representing the number of institutions from each country.\n",
+    "\n",
+    "You **have** already computed a **DataFrame** `num_institutions` (in Question 5) containing the **Country**, and the **Number of Insititutions** data. Run the following cell just to confirm that the variable has not changed its values since you defined it in Question 5."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71138c9d",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"q20\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26a5305a",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "Now, **plot** `num_institutions` as **pie plot** with the **title** *Number of Institutions*.\n",
+    "\n",
+    "You **must** use the `pie_plot` function to create the **pie plot**. The **colors** do **not** matter, but the plot **must** be titled `Number of Institutions`, and **must** be labelled as in the sample output below.\n",
+    "\n",
+    "**Important Warning:** `public_tests.py` can check that the **DataFrame** is correct, but it **cannot** check if your plot appears on the screen, or whether the axes are correctly labelled. If your plot is not visible, or if it is not properly labelled, the Gradescope autograder will **deduct points**.\n",
+    "\n",
+    "Your plot should look like this:"
+   ]
+  },
+  {
+   "attachments": {
+    "q20.jpg": {
+     "image/jpeg": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "id": "e2e8acfe",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "source": [
+    "<div><img src=\"attachment:q20.jpg\" style=\"height: 300px;\"/></div>\n",
+    "\n",
+    "<center> <b>Delete</b> this cell before you submit the notebook to reduce the size of your file.</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d4b0a8e",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:19.644413Z",
+     "iopub.status.busy": "2023-12-06T02:04:19.643408Z",
+     "iopub.status.idle": "2023-12-06T02:04:19.893611Z",
+     "shell.execute_reply": "2023-12-06T02:04:19.892597Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# create the pie plot using the DataFrame 'num_institutions' titled \"Number of Institutions\"\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aefcc1c3",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "**Food for thought:** It seems that we'll run out of colors! How can we make it so that **no two neighbors share a color**? You'll probably have to look online."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaf28097",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:19.897610Z",
+     "iopub.status.busy": "2023-12-06T02:04:19.897610Z",
+     "iopub.status.idle": "2023-12-06T02:04:19.902753Z",
+     "shell.execute_reply": "2023-12-06T02:04:19.901738Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Food for thought is an entirely OPTIONAL exercise\n",
+    "# you may leave your thoughts here as a comment if you wish to\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10423208",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "### Closing the database connection:\n",
+    "\n",
+    "Now, before you **submit** your notebook, you **must** **close** your connection `conn`. Not doing this might make **Gradescope fail**. Additionally, **delete** the example images provided with plot questions to save space, if your notebook file is too large for submission. You can **delete** any cell by selecting the cell, hitting the `Esc` key once, and then hitting the `d` key **twice**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01d7cf09",
+   "metadata": {
+    "deletable": true,
+    "editable": true,
+    "execution": {
+     "iopub.execute_input": "2023-12-06T02:04:19.906753Z",
+     "iopub.status.busy": "2023-12-06T02:04:19.906753Z",
+     "iopub.status.idle": "2023-12-06T02:04:19.912111Z",
+     "shell.execute_reply": "2023-12-06T02:04:19.911080Z"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# close your connection here\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d15acc12",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"general_deductions\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31e91cf5",
+   "metadata": {
+    "deletable": true,
+    "editable": true
+   },
+   "outputs": [],
+   "source": [
+    "grader.check(\"summary\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6fcd2b1",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    "## Submission\n",
+    "It is recommended that at this stage, you Restart and Run all Cells in your notebook.\n",
+    "That will automatically save your work and generate a zip file for you to submit.\n",
+    "\n",
+    "**SUBMISSION INSTRUCTIONS**:\n",
+    "1. **Upload** the zipfile to Gradescope.\n",
+    "2. If you completed the project with a **partner**, make sure to **add their name** by clicking \"Add Group Member\"\n",
+    "in Gradescope when uploading the zip file.\n",
+    "3. Check **Gradescope** results as soon as the auto-grader execution gets completed.\n",
+    "4. Your **final score** for this project is the score that you see on **Gradescope**.\n",
+    "5. You are **allowed** to resubmit on Gradescope as many times as you want to.\n",
+    "6. **Contact** a TA/PM if you lose any points on Gradescope for any **unclear reasons**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1654071",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "# running this cell will create a new save checkpoint for your notebook\n",
+    "from IPython.display import display, Javascript\n",
+    "display(Javascript('IPython.notebook.save_checkpoint();'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4222e304",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "!jupytext --to py p13.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5d0f665",
+   "metadata": {
+    "cell_type": "code",
+    "deletable": false,
+    "editable": false
+   },
+   "outputs": [],
+   "source": [
+    "public_tests.check_file_size(\"p13.ipynb\")\n",
+    "grader.export(pdf=False, run_tests=False, files=[\"p13.py\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9928e030",
+   "metadata": {
+    "deletable": false,
+    "editable": false
+   },
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "otter": {
+   "OK_FORMAT": true,
+   "tests": {
+    "bar_plot": {
+     "name": "bar_plot",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('bar_plot: data is not plotted correctly')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'data is not plotted correctly (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('bar_plot: legend is not deleted or axes are not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'legend is not deleted or axes are not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "conn": {
+     "name": "conn",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('conn: data structure is defined more than once')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'data structure is defined more than once (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('conn: did not close the connection to `conn` at the end')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not close the connection to `conn` at the end (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "general_deductions": {
+     "name": "general_deductions",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('general_deductions: Outputs not visible/did not save the notebook file prior to running the cell containing \"export\". We cannot see your output if you do not save before generating the zip file.')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'Outputs not visible/did not save the notebook file prior to running the cell containing \"export\". We cannot see your output if you do not save before generating the zip file. (-3)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('general_deductions: Import statements are not mentioned in the required cell at the top of the notebook.')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'Import statements are not mentioned in the required cell at the top of the notebook. (-3)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "get_regression_coeff": {
+     "name": "get_regression_coeff",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('get_regression_coeff: function logic is incorrect')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function logic is incorrect (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "get_regression_line": {
+     "name": "get_regression_line",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('get_regression_line: function logic is incorrect')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function logic is incorrect (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "horizontal_bar_plot": {
+     "name": "horizontal_bar_plot",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('horizontal_bar_plot: data is not plotted correctly')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'data is not plotted correctly (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "pie_plot": {
+     "name": "pie_plot",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('pie_plot: data is not plotted correctly')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'data is not plotted correctly (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q1": {
+     "name": "q1",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q1', uw_stats.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q1: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q1: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q10": {
+     "name": "q10",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q10', top_ranked_inter_faculty[[\"Faculty Student\", \"International Students\"]].reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q10: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q10: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q10: `scatter_plot` function is not used to plot', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`scatter_plot` function is not used to plot (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q10: plot is not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q11": {
+     "name": "q11",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q11', uk_inter_score_corr)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q11: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q11: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q12": {
+     "name": "q12",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q12', cit_per_inter_score_corr)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q12: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q12: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q13": {
+     "name": "q13",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q13', top_cit_per_inter.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q13: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q13: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q14": {
+     "name": "q14",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q14', avg_cit_per_inter.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q14: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q14: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q15": {
+     "name": "q15",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q15', max_cit_per_inter.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q15: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q15: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q16": {
+     "name": "q16",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q16', country_citations_inter.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q16: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q16: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q16: `horizontal_bar_plot` function is not used to plot', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`horizontal_bar_plot` function is not used to plot (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q16: plot is not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q17": {
+     "name": "q17",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q17', overall_rank[[\"Rank\", \"Overall\", \"fit\"]].reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q17: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q17: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q17: `regression_line_plot` function is not used to plot', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`regression_line_plot` function is not used to plot (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q17: plot is not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q18": {
+     "name": "q18",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q18', inverse_overall_rank[[\"Rank\", \"Inverse Overall\", \"fit\"]].reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q18: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q18: `get_regression_line` function is not used to answer', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_regression_line` function is not used to answer (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q18: `regression_line_plot` function is not used to plot', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`regression_line_plot` function is not used to plot (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q18: plot is not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q19": {
+     "name": "q19",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q19', rank_score_72)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q19: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q19: `get_regression_coeff` function is not used to answer', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_regression_coeff` function is not used to answer (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q2": {
+     "name": "q2",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q2', japan_top_10_inter.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q2: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q2: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q20": {
+     "name": "q20",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q20', num_institutions.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q20: `pie_plot` function is not used to plot', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`pie_plot` function is not used to plot (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q20: plot is not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> public_tests.rubric_check('q20: public tests')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q3": {
+     "name": "q3",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q3', us_top_10_rep.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q3: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q3: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q4": {
+     "name": "q4",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q4', top_10_countries.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q4: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q4: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q5": {
+     "name": "q5",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q5', num_institutions.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q5: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q5: `bar_plot` function is not used to plot', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`bar_plot` function is not used to plot (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q5: plot is not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> public_tests.rubric_check('q5: public tests')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q6": {
+     "name": "q6",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q6', top_10_total_score.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q6: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q6: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q6: `bar_plot` function is not used to plot', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`bar_plot` function is not used to plot (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q6: plot is not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q7": {
+     "name": "q7",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q7', top_10_inter_score.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q7: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q7: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q8": {
+     "name": "q8",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q8', citations_overall.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q8: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q8: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q8: `scatter_plot` function is not used to plot', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`scatter_plot` function is not used to plot (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q8: plot is not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "q9": {
+     "name": "q9",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.check('q9', reputations_usa.reset_index(drop=True))\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q9: did not use SQL to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'did not use SQL to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q9: incorrect logic is used to answer')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'incorrect logic is used to answer (-2)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q9: `scatter_plot` function is not used to plot', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`scatter_plot` function is not used to plot (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('q9: plot is not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'plot is not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "regression_line_plot": {
+     "name": "regression_line_plot",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('regression_line_plot: `get_regression_line` function is not used to answer', False)\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - '`get_regression_line` function is not used to answer (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('regression_line_plot: function does not create correct scatter plot or the correct line plot using `df[\"fit\"]`')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'function does not create correct scatter plot or the correct line plot using `df[\"fit\"]` (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "scatter_plot": {
+     "name": "scatter_plot",
+     "points": 0,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('scatter_plot: data is not plotted correctly')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'data is not plotted correctly (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        },
+        {
+         "code": ">>> \n>>> public_tests.rubric_check('scatter_plot: legend is not deleted or axes are not properly labeled')\nAll test cases passed!\n",
+         "hidden": false,
+         "locked": false,
+         "success_message": "Note that the Gradescope autograder will deduct points if your code fails the following rubric point - 'legend is not deleted or axes are not properly labeled (-1)'.The public tests cannot determine if your code satisfies these requirements. Verify your code manually."
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    },
+    "summary": {
+     "name": "summary",
+     "points": 127,
+     "suites": [
+      {
+       "cases": [
+        {
+         "code": ">>> public_tests.get_summary()\nTotal Score: 100/100\n",
+         "hidden": false,
+         "locked": false
+        }
+       ],
+       "scored": true,
+       "setup": "",
+       "teardown": "",
+       "type": "doctest"
+      }
+     ]
+    }
+   }
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/p13/public_tests.py b/p13/public_tests.py
new file mode 100644
index 0000000000000000000000000000000000000000..11b5a81d36b934530e302d40b0f5c53cdcf0bb9f
--- /dev/null
+++ b/p13/public_tests.py
@@ -0,0 +1,813 @@
+#!/usr/bin/python
+# +
+import os, json, math, copy
+from collections import namedtuple
+import pandas as pd
+import bs4
+from numpy import nan
+
+HIDDEN_FILE = os.path.join("hidden", "hidden_tests.py")
+if os.path.exists(HIDDEN_FILE):
+    import hidden.hidden_tests as hidn
+# -
+
+MAX_FILE_SIZE = 750 # units - KB
+REL_TOL = 6e-04  # relative tolerance for floats
+ABS_TOL = 15e-03  # absolute tolerance for floats
+TOTAL_SCORE = 100 # total score for the project
+
+DF_FILE = 'expected_dfs.html'
+PLOT_FILE = 'expected_plots.json'
+
+PASS = "All test cases passed!"
+
+TEXT_FORMAT = "TEXT_FORMAT"  # question type when expected answer is a type, str, int, float, or bool
+TEXT_FORMAT_UNORDERED_LIST = "TEXT_FORMAT_UNORDERED_LIST"  # question type when the expected answer is a list or a set where the order does *not* matter
+TEXT_FORMAT_ORDERED_LIST = "TEXT_FORMAT_ORDERED_LIST"  # question type when the expected answer is a list or tuple where the order does matter
+TEXT_FORMAT_DICT = "TEXT_FORMAT_DICT"  # question type when the expected answer is a dictionary
+TEXT_FORMAT_SPECIAL_ORDERED_LIST = "TEXT_FORMAT_SPECIAL_ORDERED_LIST"  # question type when the expected answer is a list where order does matter, but with possible ties. Elements are ordered according to values in special_ordered_json (with ties allowed)
+TEXT_FORMAT_NAMEDTUPLE = "TEXT_FORMAT_NAMEDTUPLE"  # question type when expected answer is a namedtuple
+PNG_FORMAT_SCATTER = "PNG_FORMAT_SCATTER" # question type when the expected answer is a scatter plot
+HTML_FORMAT = "HTML_FORMAT" # question type when the expected answer is a DataFrame
+FILE_JSON_FORMAT = "FILE_JSON_FORMAT" # question type when the expected answer is a JSON file
+SLASHES = "SLASHES" # question SUFFIX when expected answer contains paths with slashes
+
+def get_expected_format():
+    """get_expected_format() returns a dict mapping each question to the format
+    of the expected answer."""
+    expected_format = {'q1': 'HTML_FORMAT',
+                       'q2': 'HTML_FORMAT',
+                       'q3': 'HTML_FORMAT',
+                       'q4': 'HTML_FORMAT',
+                       'q5': 'HTML_FORMAT',
+                       'q6': 'HTML_FORMAT',
+                       'q7': 'HTML_FORMAT',
+                       'q8': 'HTML_FORMAT',
+                       'q9': 'HTML_FORMAT',
+                       'q10': 'HTML_FORMAT',
+                       'q11': 'TEXT_FORMAT',
+                       'q12': 'TEXT_FORMAT',
+                       'q13': 'HTML_FORMAT',
+                       'q14': 'HTML_FORMAT',
+                       'q15': 'HTML_FORMAT',
+                       'q16': 'HTML_FORMAT',
+                       'q17': 'HTML_FORMAT',
+                       'q18': 'HTML_FORMAT',
+                       'q19': 'TEXT_FORMAT',
+                       'q20': 'HTML_FORMAT'}
+    return expected_format
+
+
+def get_expected_json():
+    """get_expected_json() returns a dict mapping each question to the expected
+    answer (if the format permits it)."""
+    expected_json = {'q11': 0.4534457221342218, 'q12': 0.5943163420092246, 'q19': 59}
+    return expected_json
+
+
+def get_special_json():
+    """get_special_json() returns a dict mapping each question to the expected
+    answer stored in a special format as a list of tuples. Each tuple contains
+    the element expected in the list, and its corresponding value. Any two
+    elements with the same value can appear in any order in the actual list,
+    but if two elements have different values, then they must appear in the
+    same order as in the expected list of tuples."""
+    special_json = {}
+    return special_json
+
+
+def compare(expected, actual, q_format=TEXT_FORMAT):
+    """compare(expected, actual) is used to compare when the format of
+    the expected answer is known for certain."""
+    try:
+        if SLASHES in q_format:
+            q_format = q_format.replace(SLASHES, "").strip("_ ")
+            expected = clean_slashes(expected)
+            actual = clean_slashes(actual)
+            
+        if q_format == TEXT_FORMAT:
+            return simple_compare(expected, actual)
+        elif q_format == TEXT_FORMAT_UNORDERED_LIST:
+            return list_compare_unordered(expected, actual)
+        elif q_format == TEXT_FORMAT_ORDERED_LIST:
+            return list_compare_ordered(expected, actual)
+        elif q_format == TEXT_FORMAT_DICT:
+            return dict_compare(expected, actual)
+        elif q_format == TEXT_FORMAT_SPECIAL_ORDERED_LIST:
+            return list_compare_special(expected, actual)
+        elif q_format == TEXT_FORMAT_NAMEDTUPLE:
+            return namedtuple_compare(expected, actual)
+        elif q_format == PNG_FORMAT_SCATTER:
+            return compare_flip_dicts(expected, actual)
+        elif q_format == HTML_FORMAT:
+            return compare_cell_html(expected, actual)
+        elif q_format == FILE_JSON_FORMAT:
+            return compare_file_json(expected, actual)
+        else:
+            if expected != actual:
+                return "expected %s but found %s " % (repr(expected), repr(actual))
+    except:
+        if expected != actual:
+            return "expected %s but found %s " % (repr(expected), repr(actual))
+    return PASS
+
+
+def print_message(expected, actual, complete_msg=True):
+    """print_message(expected, actual) displays a simple error message."""
+    msg = "expected %s" % (repr(expected))
+    if complete_msg:
+        msg = msg + " but found %s" % (repr(actual))
+    return msg
+
+
+def simple_compare(expected, actual, complete_msg=True):
+    """simple_compare(expected, actual) is used to compare when the expected answer
+    is a type/Nones/str/int/float/bool. When the expected answer is a float,
+    the actual answer is allowed to be within the tolerance limit. Otherwise,
+    the values must match exactly, or a very simple error message is displayed."""
+    msg = PASS
+    if 'numpy' in repr(type((actual))):
+        actual = actual.item()
+    if isinstance(expected, type):
+        if expected != actual:
+            if isinstance(actual, type):
+                msg = "expected %s but found %s" % (expected.__name__, actual.__name__)
+            else:
+                msg = "expected %s but found %s" % (expected.__name__, repr(actual))
+            return msg
+    elif not isinstance(actual, type(expected)):
+        if not (isinstance(expected, (float, int)) and isinstance(actual, (float, int))) and not is_namedtuple(expected):
+            return "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+    if isinstance(expected, float):
+        if not math.isclose(actual, expected, rel_tol=REL_TOL):
+            msg = print_message(expected, actual, complete_msg)
+    elif isinstance(expected, (list, tuple)) or is_namedtuple(expected):
+        new_msg = print_message(expected, actual, complete_msg)
+        if len(expected) != len(actual):
+            return new_msg
+        for i in range(len(expected)):
+            val = simple_compare(expected[i], actual[i])
+            if val != PASS:
+                return new_msg
+    elif isinstance(expected, dict):
+        new_msg = print_message(expected, actual, complete_msg)
+        if len(expected) != len(actual):
+            return new_msg
+        val = simple_compare(sorted(list(expected.keys())), sorted(list(actual.keys())))
+        if val != PASS:
+            return new_msg
+        for key in expected:
+            val = simple_compare(expected[key], actual[key])
+            if val != PASS:
+                return new_msg
+    else:
+        if expected != actual:
+            msg = print_message(expected, actual, complete_msg)
+    return msg
+
+
+def intelligent_compare(expected, actual, obj=None):
+    """intelligent_compare(expected, actual) is used to compare when the
+    data type of the expected answer is not known for certain, and default
+    assumptions  need to be made."""
+    if obj == None:
+        obj = type(expected).__name__
+    if is_namedtuple(expected):
+        msg = namedtuple_compare(expected, actual)
+    elif isinstance(expected, (list, tuple)):
+        msg = list_compare_ordered(expected, actual, obj)
+    elif isinstance(expected, set):
+        msg = list_compare_unordered(expected, actual, obj)
+    elif isinstance(expected, (dict)):
+        msg = dict_compare(expected, actual)
+    else:
+        msg = simple_compare(expected, actual)
+    msg = msg.replace("CompDict", "dict").replace("CompSet", "set").replace("NewNone", "None")
+    return msg
+
+
+def is_namedtuple(obj, init_check=True):
+    """is_namedtuple(obj) returns True if `obj` is a namedtuple object
+    defined in the test file."""
+    bases = type(obj).__bases__
+    if len(bases) != 1 or bases[0] != tuple:
+        return False
+    fields = getattr(type(obj), '_fields', None)
+    if not isinstance(fields, tuple):
+        return False
+    if init_check and not type(obj).__name__ in [nt.__name__ for nt in _expected_namedtuples]:
+        return False
+    return True
+
+
+def list_compare_ordered(expected, actual, obj=None):
+    """list_compare_ordered(expected, actual) is used to compare when the
+    expected answer is a list/tuple, where the order of the elements matters."""
+    msg = PASS
+    if not isinstance(actual, type(expected)):
+        msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+        return msg
+    if obj == None:
+        obj = type(expected).__name__
+    for i in range(len(expected)):
+        if i >= len(actual):
+            msg = "at index %d of the %s, expected missing %s" % (i, obj, repr(expected[i]))
+            break
+        val = intelligent_compare(expected[i], actual[i], "sub" + obj)
+        if val != PASS:
+            msg = "at index %d of the %s, " % (i, obj) + val
+            break
+    if len(actual) > len(expected) and msg == PASS:
+        msg = "at index %d of the %s, found unexpected %s" % (len(expected), obj, repr(actual[len(expected)]))
+    if len(expected) != len(actual):
+        msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+
+    if len(expected) > 0:
+        try:
+            if msg != PASS and list_compare_unordered(expected, actual, obj) == PASS:
+                msg = msg + " (%s may not be ordered as required)" % (obj)
+        except:
+            pass
+    return msg
+
+
+def list_compare_helper(larger, smaller):
+    """list_compare_helper(larger, smaller) is a helper function which takes in
+    two lists of possibly unequal sizes and finds the item that is not present
+    in the smaller list, if there is such an element."""
+    msg = PASS
+    j = 0
+    for i in range(len(larger)):
+        if i == len(smaller):
+            msg = "expected %s" % (repr(larger[i]))
+            break
+        found = False
+        while not found:
+            if j == len(smaller):
+                val = simple_compare(larger[i], smaller[j - 1], complete_msg=False)
+                break
+            val = simple_compare(larger[i], smaller[j], complete_msg=False)
+            j += 1
+            if val == PASS:
+                found = True
+                break
+        if not found:
+            msg = val
+            break
+    return msg
+
+class NewNone():
+    """alternate class in place of None, which allows for comparison with
+    all other data types."""
+    def __str__(self):
+        return 'None'
+    def __repr__(self):
+        return 'None'
+    def __lt__(self, other):
+        return True
+    def __le__(self, other):
+        return True
+    def __gt__(self, other):
+        return False
+    def __ge__(self, other):
+        return other == None
+    def __eq__(self, other):
+        return other == None
+    def __ne__(self, other):
+        return other != None
+
+class CompDict(dict):
+    """subclass of dict, which allows for comparison with other dicts."""
+    def __init__(self, vals):
+        super(self.__class__, self).__init__(vals)
+        if type(vals) == CompDict:
+            self.val = vals.val
+        elif isinstance(vals, dict):
+            self.val = self.get_equiv(vals)
+        else:
+            raise TypeError("'%s' object cannot be type casted to CompDict class" % type(vals).__name__)
+
+    def get_equiv(self, vals):
+        val = []
+        for key in sorted(list(vals.keys())):
+            val.append((key, vals[key]))
+        return val
+
+    def __str__(self):
+        return str(dict(self.val))
+    def __repr__(self):
+        return repr(dict(self.val))
+    def __lt__(self, other):
+        return self.val < CompDict(other).val
+    def __le__(self, other):
+        return self.val <= CompDict(other).val
+    def __gt__(self, other):
+        return self.val > CompDict(other).val
+    def __ge__(self, other):
+        return self.val >= CompDict(other).val
+    def __eq__(self, other):
+        return self.val == CompDict(other).val
+    def __ne__(self, other):
+        return self.val != CompDict(other).val
+
+class CompSet(set):
+    """subclass of set, which allows for comparison with other sets."""
+    def __init__(self, vals):
+        super(self.__class__, self).__init__(vals)
+        if type(vals) == CompSet:
+            self.val = vals.val
+        elif isinstance(vals, set):
+            self.val = self.get_equiv(vals)
+        else:
+            raise TypeError("'%s' object cannot be type casted to CompSet class" % type(vals).__name__)
+
+    def get_equiv(self, vals):
+        return sorted(list(vals))
+
+    def __str__(self):
+        return str(set(self.val))
+    def __repr__(self):
+        return repr(set(self.val))
+    def __getitem__(self, index):
+        return self.val[index]
+    def __lt__(self, other):
+        return self.val < CompSet(other).val
+    def __le__(self, other):
+        return self.val <= CompSet(other).val
+    def __gt__(self, other):
+        return self.val > CompSet(other).val
+    def __ge__(self, other):
+        return self.val >= CompSet(other).val
+    def __eq__(self, other):
+        return self.val == CompSet(other).val
+    def __ne__(self, other):
+        return self.val != CompSet(other).val
+
+def make_sortable(item):
+    """make_sortable(item) replaces all Nones in `item` with an alternate
+    class that allows for comparison with str/int/float/bool/list/set/tuple/dict.
+    It also replaces all dicts (and sets) with a subclass that allows for
+    comparison with other dicts (and sets)."""
+    if item == None:
+        return NewNone()
+    elif isinstance(item, (type, str, int, float, bool)):
+        return item
+    elif isinstance(item, (list, set, tuple)):
+        new_item = []
+        for subitem in item:
+            new_item.append(make_sortable(subitem))
+        if is_namedtuple(item):
+            return type(item)(*new_item)
+        elif isinstance(item, set):
+            return CompSet(new_item)
+        else:
+            return type(item)(new_item)
+    elif isinstance(item, dict):
+        new_item = {}
+        for key in item:
+            new_item[key] = make_sortable(item[key])
+        return CompDict(new_item)
+    return item
+
+def list_compare_unordered(expected, actual, obj=None):
+    """list_compare_unordered(expected, actual) is used to compare when the
+    expected answer is a list/set where the order of the elements does not matter."""
+    msg = PASS
+    if not isinstance(actual, type(expected)):
+        msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+        return msg
+    if obj == None:
+        obj = type(expected).__name__
+
+    try:
+        sort_expected = sorted(make_sortable(expected))
+        sort_actual = sorted(make_sortable(actual))
+    except:
+        return "unexpected datatype found in %s; expected entries of type %s" % (obj, obj, type(expected[0]).__name__)
+
+    if len(actual) == 0 and len(expected) > 0:
+        msg = "in the %s, missing " % (obj) + sort_expected[0]
+    elif len(actual) > 0 and len(expected) > 0:
+        val = intelligent_compare(sort_expected[0], sort_actual[0])
+        if val.startswith("expected to find type"):
+            msg = "in the %s, " % (obj) + simple_compare(sort_expected[0], sort_actual[0])
+        else:
+            if len(expected) > len(actual):
+                msg = "in the %s, missing " % (obj) + list_compare_helper(sort_expected, sort_actual)
+            elif len(expected) < len(actual):
+                msg = "in the %s, found un" % (obj) + list_compare_helper(sort_actual, sort_expected)
+            if len(expected) != len(actual):
+                msg = msg + " (found %d entries in %s, but expected %d)" % (len(actual), obj, len(expected))
+                return msg
+            else:
+                val = list_compare_helper(sort_expected, sort_actual)
+                if val != PASS:
+                    msg = "in the %s, missing " % (obj) + val + ", but found un" + list_compare_helper(sort_actual,
+                                                                                               sort_expected)
+    return msg
+
+
+def namedtuple_compare(expected, actual):
+    """namedtuple_compare(expected, actual) is used to compare when the
+    expected answer is a namedtuple defined in the test file."""
+    msg = PASS
+    if not is_namedtuple(actual, False):
+        return "expected to find type %s namedtuple but found type %s" % (type(expected).__name__, type(actual).__name__)
+    if type(expected).__name__ != type(actual).__name__:
+        return "expected to find type %s namedtuple but found type %s namedtuple" % (type(expected).__name__, type(actual).__name__)
+    expected_fields = expected._fields
+    actual_fields = actual._fields
+    msg = list_compare_ordered(list(expected_fields), list(actual_fields), "namedtuple attributes")
+    if msg != PASS:
+        return msg
+    for field in expected_fields:
+        val = intelligent_compare(getattr(expected, field), getattr(actual, field))
+        if val != PASS:
+            msg = "at attribute %s of namedtuple %s, " % (field, type(expected).__name__) + val
+            return msg
+    return msg
+
+
+def clean_slashes(item):
+    """clean_slashes()"""
+    if isinstance(item, str):
+        replaced_item = item.replace("\\", os.path.sep).replace("/", os.path.sep)
+        new_item = [type(item)(subitem) for subitem in os.path.split(replaced_item)]
+        return new_item
+    elif item == None or isinstance(item, (type, int, float, bool)):
+        return item
+    elif isinstance(item, (list, tuple, set)) or is_namedtuple(item):
+        new_item = []
+        for subitem in item:
+            new_item.append(clean_slashes(subitem))
+        if is_namedtuple(item):
+            return type(item)(*new_item)
+        else:
+            return type(item)(new_item)
+    elif isinstance(item, dict):
+        new_item = {}
+        for key in item:
+            new_item[clean_slashes(key)] = clean_slashes(item[key])
+        return item
+
+
+def list_compare_special_initialize(special_expected):
+    """list_compare_special_initialize(special_expected) takes in the special
+    ordering stored as a sorted list of items, and returns a list of lists
+    where the ordering among the inner lists does not matter."""
+    latest_val = None
+    clean_special = []
+    for row in special_expected:
+        if latest_val == None or row[1] != latest_val:
+            clean_special.append([])
+            latest_val = row[1]
+        clean_special[-1].append(row[0])
+    return clean_special
+
+
+def list_compare_special(special_expected, actual):
+    """list_compare_special(special_expected, actual) is used to compare when the
+    expected answer is a list with special ordering defined in `special_expected`."""
+    msg = PASS
+    expected_list = []
+    special_order = list_compare_special_initialize(special_expected)
+    for expected_item in special_order:
+        expected_list.extend(expected_item)
+    val = list_compare_unordered(expected_list, actual)
+    if val != PASS:
+        return val
+    i = 0
+    for expected_item in special_order:
+        j = len(expected_item)
+        actual_item = actual[i: i + j]
+        val = list_compare_unordered(expected_item, actual_item)
+        if val != PASS:
+            if j == 1:
+                msg = "at index %d " % (i) + val
+            else:
+                msg = "between indices %d and %d " % (i, i + j - 1) + val
+            msg = msg + " (list may not be ordered as required)"
+            break
+        i += j
+    return msg
+
+
+def dict_compare(expected, actual, obj=None):
+    """dict_compare(expected, actual) is used to compare when the expected answer
+    is a dict."""
+    msg = PASS
+    if not isinstance(actual, type(expected)):
+        msg = "expected to find type %s but found type %s" % (type(expected).__name__, type(actual).__name__)
+        return msg
+    if obj == None:
+        obj = type(expected).__name__
+
+    expected_keys = list(expected.keys())
+    actual_keys = list(actual.keys())
+    val = list_compare_unordered(expected_keys, actual_keys, obj)
+
+    if val != PASS:
+        msg = "bad keys in %s: " % (obj) + val
+    if msg == PASS:
+        for key in expected:
+            new_obj = None
+            if isinstance(expected[key], (list, tuple, set)):
+                new_obj = 'value'
+            elif isinstance(expected[key], dict):
+                new_obj = 'sub' + obj
+            val = intelligent_compare(expected[key], actual[key], new_obj)
+            if val != PASS:
+                msg = "incorrect value for key %s in %s: " % (repr(key), obj) + val
+    return msg
+
+
+def is_flippable(item):
+    """is_flippable(item) determines if the given dict of lists has lists of the
+    same length and is therefore flippable."""
+    item_lens = set(([str(len(item[key])) for key in item]))
+    if len(item_lens) == 1:
+        return PASS
+    else:
+        return "found lists of lengths %s" % (", ".join(list(item_lens)))
+
+def flip_dict_of_lists(item):
+    """flip_dict_of_lists(item) flips a dict of lists into a list of dicts if the
+    lists are of same length."""
+    new_item = []
+    length = len(list(item.values())[0])
+    for i in range(length):
+        new_dict = {}
+        for key in item:
+            new_dict[key] = item[key][i]
+        new_item.append(new_dict)
+    return new_item
+
+def compare_flip_dicts(expected, actual):
+    """compare_flip_dicts(expected, actual) flips a dict of lists (or dicts) into
+    a list of dicts (or dict of dicts) and then compares the list ignoring order."""
+    msg = PASS
+    example_item = list(expected.values())[0]
+    if isinstance(example_item, (list, tuple)):
+        val = is_flippable(actual)
+        if val != PASS:
+            msg = "expected to find lists of length %d, but " % (len(example_item)) + val
+            return msg
+        msg = list_compare_unordered(flip_dict_of_lists(expected), flip_dict_of_lists(actual), "lists")
+    elif isinstance(example_item, dict):
+        expected_keys = list(example_item.keys())
+        for key in actual:
+            val = list_compare_unordered(expected_keys, list(actual[key].keys()), "dictionary %s" % key)
+            if val != PASS:
+                return val
+        for cat_key in expected_keys:
+            expected_category = {}
+            actual_category = {}
+            for key in expected:
+                expected_category[key] = expected[key][cat_key]
+                actual_category[key] = actual[key][cat_key]
+            val = list_compare_unordered(flip_dict_of_lists(expected_category), flip_dict_of_lists(actual_category), "category " + repr(cat_key))
+            if val != PASS:
+                return val
+    return msg
+
+
+def get_expected_tables():
+    """get_expected_tables() reads the html file with the expected DataFrames
+    and returns a dict mapping each question to a html table."""
+    if not os.path.exists(DF_FILE):
+        return None
+
+    expected_tables = {}
+    f = open(DF_FILE, encoding='utf-8')
+    soup = bs4.BeautifulSoup(f.read(), 'html.parser')
+    f.close()
+
+    tables = soup.find_all('table')
+    for table in tables:
+        expected_tables[table.get("data-question")] = table
+
+    return expected_tables
+
+def parse_table(table):
+    """parse_table(table) takes in a table as a html string and returns
+    a dict mapping each row and column index to the value at that position."""
+    rows = []
+    for tr in table.find_all('tr'):
+        rows.append([])
+        for cell in tr.find_all(['td', 'th']):
+            rows[-1].append(cell.get_text().strip("\n "))
+
+    cells = {}
+    for r in range(1, len(rows)):
+        for c in range(1, len(rows[0])):
+            rname = rows[r][0]
+            cname = rows[0][c]
+            cells[(rname,cname)] = rows[r][c]
+    return cells
+
+
+def get_expected_namedtuples():
+    """get_expected_namedtuples() defines the required namedtuple objects
+    globally. It also returns a tuple of the classes."""
+    expected_namedtuples = []
+    
+    return tuple(expected_namedtuples)
+
+_expected_namedtuples = get_expected_namedtuples()
+
+
+def parse_df(item):
+    """parse_df(item) takes in a DataFrame input in any format (i.e, a DataFrame, or as html string)
+    and extracts the table in the DataFrame as a bs4.BeautifulSoup object"""
+    if isinstance(item, (pd.Series)):
+        item = pd.DataFrame(item)
+    if isinstance(item, (pd.DataFrame)):
+        item = item.to_html()
+    if isinstance(item, (str)):
+        item = bs4.BeautifulSoup(item, 'html.parser').find('table')
+    if isinstance(item, (bs4.element.Tag)):
+        return item
+    return "item could not be parsed"
+
+
+def compare_cell_html(expected, actual):
+    """compare_cell_html(expected, actual) is used to compare when the
+    expected answer is a DataFrame stored in the `expected_dfs` html file."""
+    try:
+        expected_table = parse_df(expected)
+        actual_table = parse_df(actual)
+    except Exception as e:
+        return "expected to find type DataFrame but found type %s instead" % type(actual).__name__
+    
+    if not isinstance(expected_table, (bs4.element.Tag)) or not isinstance(actual_table, (bs4.element.Tag)):
+        return "expected to find type DataFrames but found types %s and %s instead" % (type(expected).__name__, type(actual).__name__)
+    expected_cells = parse_table(expected_table)
+    actual_cells = parse_table(actual_table)
+
+    expected_cols = list(set(["column %s" % (loc[1]) for loc in expected_cells]))
+    actual_cols = list(set(["column %s" % (loc[1]) for loc in actual_cells]))
+    msg = list_compare_unordered(expected_cols, actual_cols, "DataFrame")
+    if msg != PASS:
+        return msg
+
+    expected_rows = list(set(["row index %s" % (loc[0]) for loc in expected_cells]))
+    actual_rows = list(set(["row index %s" % (loc[0]) for loc in actual_cells]))
+    msg = list_compare_unordered(expected_rows, actual_rows, "DataFrame")
+    if msg != PASS:
+        return msg
+
+    for location, expected in expected_cells.items():
+        location_name = "column {} at index {}".format(location[1], location[0])
+        actual = actual_cells.get(location, None)
+        if actual == None:
+            return "in %s, expected to find %s" % (location_name, repr(expected))
+        try:
+            actual_ans = float(actual)
+            expected_ans = float(expected)
+            if math.isnan(actual_ans) and math.isnan(expected_ans):
+                continue
+        except Exception as e:
+            actual_ans, expected_ans = actual, expected
+        msg = simple_compare(expected_ans, actual_ans)
+        if msg != PASS:
+            return "in %s, " % location_name + msg
+    return PASS
+
+
+def get_expected_plots():
+    """get_expected_plots() reads the json file with the expected plot data
+    and returns a dict mapping each question to a dictionary with the plots data."""
+    if not os.path.exists(PLOT_FILE):
+        return None
+
+    f = open(PLOT_FILE, encoding='utf-8')
+    expected_plots = json.load(f)
+    f.close()
+    return expected_plots
+
+
+def compare_file_json(expected, actual):
+    """compare_file_json(expected, actual) is used to compare when the
+    expected answer is a JSON file."""
+    msg = PASS
+    if not os.path.isfile(expected):
+        return "file %s not found; make sure it is downloaded and stored in the correct directory" % (expected)
+    elif not os.path.isfile(actual):
+        return "file %s not found; make sure that you have created the file with the correct name" % (actual)
+    try:
+        e = open(expected, encoding='utf-8')
+        expected_data = json.load(e)
+        e.close()
+    except json.JSONDecodeError:
+        return "file %s is broken and cannot be parsed; please delete and redownload the file correctly" % (expected)
+    try:
+        a = open(actual, encoding='utf-8')
+        actual_data = json.load(a)
+        a.close()
+    except json.JSONDecodeError:
+        return "file %s is broken and cannot be parsed" % (actual)
+    if isinstance(expected_data, list):
+        msg = list_compare_ordered(expected_data, actual_data, 'file ' + actual)
+    elif isinstance(expected_data, dict):
+        msg = dict_compare(expected_data, actual_data)
+    return msg
+
+
+_expected_json = get_expected_json()
+_special_json = get_special_json()
+_expected_plots = get_expected_plots()
+_expected_tables = get_expected_tables()
+_expected_format = get_expected_format()
+
+def check(qnum, actual):
+    """check(qnum, actual) is used to check if the answer in the notebook is
+    the correct answer, and provide useful feedback if the answer is incorrect."""
+    msg = PASS
+    error_msg = "<b style='color: red;'>ERROR:</b> "
+    q_format = _expected_format[qnum]
+
+    if q_format == TEXT_FORMAT_SPECIAL_ORDERED_LIST:
+        expected = _special_json[qnum]
+    elif q_format == PNG_FORMAT_SCATTER:
+        if _expected_plots == None:
+            msg = error_msg + "file %s not parsed; make sure it is downloaded and stored in the correct directory" % (PLOT_FILE)
+        else:
+            expected = _expected_plots[qnum]
+    elif q_format == HTML_FORMAT:
+        if _expected_tables == None:
+            msg = error_msg + "file %s not parsed; make sure it is downloaded and stored in the correct directory" % (DF_FILE)
+        else:
+            expected = _expected_tables[qnum]
+    else:
+        expected = _expected_json[qnum]
+
+    if msg != PASS:
+        print(msg)
+    else:
+        msg = compare(expected, actual, q_format)
+        if msg != PASS:
+            msg = error_msg + msg
+        print(msg)
+
+
+def check_file_size(path):
+    """check_file_size(path) throws an error if the file is too big to display
+    on Gradescope."""
+    size = os.path.getsize(path)
+    assert size < MAX_FILE_SIZE * 10**3, "Your file is too big to be displayed by Gradescope; please delete unnecessary output cells so your file size is < %s KB" % MAX_FILE_SIZE
+
+
+def reset_hidden_tests():
+    """reset_hidden_tests() resets all hidden tests on the Gradescope autograder where the hidden test file exists"""
+    if not os.path.exists(HIDDEN_FILE):
+        return
+    hidn.reset_hidden_tests()
+
+def rubric_check(rubric_point, ignore_past_errors=True):
+    """rubric_check(rubric_point) uses the hidden test file on the Gradescope autograder to grade the `rubric_point`"""
+    if not os.path.exists(HIDDEN_FILE):
+        print(PASS)
+        return
+    error_msg_1 = "ERROR: "
+    error_msg_2 = "TEST DETAILS: "
+    try:
+        msg = hidn.rubric_check(rubric_point, ignore_past_errors)
+    except:
+        msg = "hidden tests crashed before execution"
+    if msg != PASS:
+        hidn.make_deductions(rubric_point)
+        if msg == "public tests failed":
+            comment = "The public tests have failed, so you will not receive any points for this question."
+            comment += "\nPlease confirm that the public tests pass locally before submitting."
+        elif msg == "answer is hardcoded":
+            comment = "In the datasets for testing hardcoding, all numbers are replaced with random values."
+            comment += "\nIf the answer is the same as in the original dataset for all these datasets"
+            comment += "\ndespite this, that implies that the answer in the notebook is hardcoded."
+            comment += "\nYou will not receive any points for this question."
+        else:
+            comment = hidn.get_comment(rubric_point)
+        msg = error_msg_1 + msg
+        if comment != "":
+            msg = msg + "\n" + error_msg_2 + comment
+    print(msg)
+
+def get_summary():
+    """get_summary() returns the summary of the notebook using the hidden test file on the Gradescope autograder"""
+    if not os.path.exists(HIDDEN_FILE):
+        print("Total Score: %d/%d" % (TOTAL_SCORE, TOTAL_SCORE))
+        return
+    score = min(TOTAL_SCORE, hidn.get_score(TOTAL_SCORE))
+    display_msg = "Total Score: %d/%d" % (score, TOTAL_SCORE)
+    if score != TOTAL_SCORE:
+        display_msg += "\n" + hidn.get_deduction_string()
+    print(display_msg)
+
+def get_score_digit(digit):
+    """get_score_digit(digit) returns the `digit` of the score using the hidden test file on the Gradescope autograder"""
+    if not os.path.exists(HIDDEN_FILE):
+        score = 2**7 - 1
+    else:
+        score = hidn.get_score(TOTAL_SCORE)
+    digits = bin(score)[2:]
+    digits = "0"*(7 - len(digits)) + digits
+    return int(digits[6 - digit])
diff --git a/p13/rubric.md b/p13/rubric.md
new file mode 100644
index 0000000000000000000000000000000000000000..2e7d93b63822c58e394091bc11b9db970574795f
--- /dev/null
+++ b/p13/rubric.md
@@ -0,0 +1,141 @@
+# Project 13 (P13) Grading Rubric
+
+
+## Code reviews
+
+- The Gradescope autograder will make deductions based on the rubric provided below.
+- To ensure that you don't lose any points, you must **review** the rubric and make sure that you have followed the instructions provided in the project correctly.
+- If you **fail** the **public tests** for a function or **hardcode** the answers to that question, you will automatically lose **all** points for that question.
+
+## Rubric
+
+### General guidelines:
+
+- Outputs not visible/did not save the notebook file prior to running the cell containing "export". We cannot see your output if you do not save before generating the zip file. (-3)
+- Import statements are not mentioned in the required cell at the top of the notebook. (-3)
+
+### Question specific guidelines:
+
+- `bar_plot` (2)
+	- data is not plotted correctly (-1)
+	- legend is not deleted or axes are not properly labeled (-1)
+
+- `scatter_plot` (2)
+	- data is not plotted correctly (-1)
+	- legend is not deleted or axes are not properly labeled (-1)
+
+- `horizontal_bar_plot` (1)
+	- data is not plotted correctly (-1)
+
+- `pie_plot` (1)
+	- data is not plotted correctly (-1)
+
+- `get_regression_coeff` (1)
+	- function logic is incorrect (-1)
+
+- `get_regression_line` (1)
+	- function logic is incorrect (-1)
+
+- `regression_line_plot` (2)
+	- `get_regression_line` function is not used to answer (-1)
+	- function does not create correct scatter plot or the correct line plot using `df["fit"]` (-1)
+
+- `conn` (3)
+	- data structure is defined more than once (-1)
+	- did not close the connection to `conn` at the end (-2)
+
+- q1 (3)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-1)
+
+- q2 (4)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+
+- q3 (4)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+
+- q4 (4)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+
+- q5 (6)
+	- incorrect logic is used to answer (-2)
+	- `bar_plot` function is not used to plot (-1)
+	- plot is not properly labeled (-1)
+
+- q6 (6)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+	- `bar_plot` function is not used to plot (-1)
+	- plot is not properly labeled (-1)
+
+- q7 (4)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+
+- q8 (6)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+	- `scatter_plot` function is not used to plot (-1)
+	- plot is not properly labeled (-1)
+
+- q9 (6)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+	- `scatter_plot` function is not used to plot (-1)
+	- plot is not properly labeled (-1)
+
+- q10 (6)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+	- `scatter_plot` function is not used to plot (-1)
+	- plot is not properly labeled (-1)
+
+- q11 (3)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-1)
+
+- q12 (3)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-1)
+
+- q13 (4)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+
+- q14 (4)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+
+- q15 (4)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+
+- q16 (6)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+	- `horizontal_bar_plot` function is not used to plot (-1)
+	- plot is not properly labeled (-1)
+
+- q17 (6)
+	- did not use SQL to answer (-2)
+	- incorrect logic is used to answer (-2)
+	- `regression_line_plot` function is not used to plot (-1)
+	- plot is not properly labeled (-1)
+
+- q18 (4)
+	- incorrect logic is used to answer (-2)
+	- `get_regression_line` function is not used to answer (-1)
+	- `regression_line_plot` function is not used to plot (-1)
+	- plot is not properly labeled (-1)
+
+- q19 (2)
+	- incorrect logic is used to answer (-1)
+	- `get_regression_coeff` function is not used to answer (-1)
+
+- q20 (4)
+	- `pie_plot` function is not used to plot (-1)
+	- plot is not properly labeled (-1)
+