diff --git a/lecture_material/21-linalg-2/21-linalg2.ipynb b/lecture_material/21-linalg-2/21-linalg2.ipynb
index 341d50965ca66d24ea4a33221054bc54229b8900..d7848a3333b4a365af8775812533c561885d9cc2 100644
--- a/lecture_material/21-linalg-2/21-linalg2.ipynb
+++ b/lecture_material/21-linalg-2/21-linalg2.ipynb
@@ -560,7 +560,7 @@
"* $c$ (known) is a vector of coefficients (our model parameters)\n",
"* $y$ (computed) are the prices\n",
"\n",
- "**Below:** what if X and y are know, and we want to find c?"
+ "**Below:** what if X and y are known, and we want to find c?"
]
},
{
@@ -1641,48 +1641,20 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": null,
"id": "f719b210",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[10, 0, 1],\n",
- " [ 2, 8, 1],\n",
- " [ 4, 4, 1],\n",
- " [ 5, 5, 1]])"
- ]
- },
- "execution_count": 44,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"X"
]
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": null,
"id": "73e680f3",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[7. ],\n",
- " [5. ],\n",
- " [5. ],\n",
- " [5.75]])"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"y"
]
@@ -1697,7 +1669,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 44,
"id": "bf3ec400",
"metadata": {},
"outputs": [
@@ -1709,7 +1681,7 @@
" [2. ]])"
]
},
- "execution_count": 46,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
@@ -1729,7 +1701,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 45,
"id": "b4c45f0e",
"metadata": {},
"outputs": [
@@ -1739,7 +1711,7 @@
"(3, 3)"
]
},
- "execution_count": 47,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
@@ -1797,7 +1769,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 46,
"id": "3506a459",
"metadata": {},
"outputs": [
@@ -1811,7 +1783,7 @@
" [8.5]])"
]
},
- "execution_count": 48,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -1830,7 +1802,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 47,
"id": "96826616-3a4a-42da-8259-35ac984dc10e",
"metadata": {},
"outputs": [
@@ -1841,7 +1813,7 @@
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mLinAlgError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[49], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m c\n",
+ "Cell \u001b[0;32mIn[47], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m c \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinalg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m c\n",
"File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36msolve\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py:373\u001b[0m, in \u001b[0;36msolve\u001b[0;34m(a, b)\u001b[0m\n\u001b[1;32m 371\u001b[0m a, _ \u001b[38;5;241m=\u001b[39m _makearray(a)\n\u001b[1;32m 372\u001b[0m _assert_stacked_2d(a)\n\u001b[0;32m--> 373\u001b[0m \u001b[43m_assert_stacked_square\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 374\u001b[0m b, wrap \u001b[38;5;241m=\u001b[39m _makearray(b)\n\u001b[1;32m 375\u001b[0m t, result_t \u001b[38;5;241m=\u001b[39m _commonType(a, b)\n",
"File \u001b[0;32m~/.local/lib/python3.8/site-packages/numpy/linalg/linalg.py:190\u001b[0m, in \u001b[0;36m_assert_stacked_square\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m 188\u001b[0m m, n \u001b[38;5;241m=\u001b[39m a\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m:]\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m m \u001b[38;5;241m!=\u001b[39m n:\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m LinAlgError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLast 2 dimensions of the array must be square\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
@@ -1864,7 +1836,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 48,
"id": "85bd8103-328d-44c2-927a-6f078c24eb91",
"metadata": {},
"outputs": [
@@ -1876,7 +1848,7 @@
" [1.55555556]])"
]
},
- "execution_count": 50,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -1896,7 +1868,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 49,
"id": "3c34b2fc-82e5-43b5-947f-d66bb4265118",
"metadata": {},
"outputs": [
@@ -1906,7 +1878,7 @@
"(3, 5)"
]
},
- "execution_count": 51,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@@ -1917,7 +1889,7 @@
},
{
"cell_type": "code",
- "execution_count": 52,
+ "execution_count": 50,
"id": "c7f1f1f6-2c54-4bf1-8570-b0857125a108",
"metadata": {},
"outputs": [
@@ -1927,7 +1899,7 @@
"(5, 3)"
]
},
- "execution_count": 52,
+ "execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
@@ -1938,7 +1910,7 @@
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 51,
"id": "8aa343c7-d041-4014-89b0-78a84f69846f",
"metadata": {},
"outputs": [
@@ -1948,7 +1920,7 @@
"(3, 3)"
]
},
- "execution_count": 53,
+ "execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
@@ -1974,7 +1946,7 @@
},
{
"cell_type": "code",
- "execution_count": 54,
+ "execution_count": 52,
"id": "243c2127",
"metadata": {},
"outputs": [
@@ -1988,7 +1960,7 @@
" [ 0.08333333, 0.08333333, -0.11111111, 0.47222222, 0.47222222]])"
]
},
- "execution_count": 54,
+ "execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
@@ -2000,7 +1972,7 @@
},
{
"cell_type": "code",
- "execution_count": 55,
+ "execution_count": 53,
"id": "1e0681e9",
"metadata": {},
"outputs": [
@@ -2014,7 +1986,7 @@
" [10, 4, 1]])"
]
},
- "execution_count": 55,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
@@ -2025,7 +1997,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 54,
"id": "3e881e59",
"metadata": {},
"outputs": [
@@ -2039,7 +2011,7 @@
" [8.5]])"
]
},
- "execution_count": 56,
+ "execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
@@ -2058,7 +2030,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 55,
"id": "33aeabe1",
"metadata": {},
"outputs": [
@@ -2072,7 +2044,7 @@
" [8.23611111]])"
]
},
- "execution_count": 57,
+ "execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
@@ -2093,7 +2065,7 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 56,
"id": "921938e2",
"metadata": {},
"outputs": [
@@ -2125,53 +2097,53 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>2.457268</td>\n",
- " <td>6.443569</td>\n",
+ " <td>5.805660</td>\n",
+ " <td>10.829947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>3.135322</td>\n",
- " <td>6.148335</td>\n",
+ " <td>4.505769</td>\n",
+ " <td>7.700637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>6.179865</td>\n",
- " <td>12.321621</td>\n",
+ " <td>8.016641</td>\n",
+ " <td>15.834128</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>4.430829</td>\n",
- " <td>8.021383</td>\n",
+ " <td>4.363379</td>\n",
+ " <td>7.800512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>5.112592</td>\n",
- " <td>10.218929</td>\n",
+ " <td>3.531965</td>\n",
+ " <td>5.616152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
- " <td>2.974205</td>\n",
- " <td>6.030322</td>\n",
+ " <td>3.591809</td>\n",
+ " <td>6.293603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
- " <td>4.791755</td>\n",
- " <td>10.157226</td>\n",
+ " <td>2.836593</td>\n",
+ " <td>5.717766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
- " <td>1.871502</td>\n",
- " <td>3.409193</td>\n",
+ " <td>2.103368</td>\n",
+ " <td>3.913067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
- " <td>4.826732</td>\n",
- " <td>9.876410</td>\n",
+ " <td>4.850006</td>\n",
+ " <td>10.235146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
- " <td>4.257244</td>\n",
- " <td>10.435702</td>\n",
+ " <td>2.299206</td>\n",
+ " <td>3.985440</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -2179,19 +2151,19 @@
],
"text/plain": [
" x y\n",
- "0 2.457268 6.443569\n",
- "1 3.135322 6.148335\n",
- "2 6.179865 12.321621\n",
- "3 4.430829 8.021383\n",
- "4 5.112592 10.218929\n",
- "5 2.974205 6.030322\n",
- "6 4.791755 10.157226\n",
- "7 1.871502 3.409193\n",
- "8 4.826732 9.876410\n",
- "9 4.257244 10.435702"
+ "0 5.805660 10.829947\n",
+ "1 4.505769 7.700637\n",
+ "2 8.016641 15.834128\n",
+ "3 4.363379 7.800512\n",
+ "4 3.531965 5.616152\n",
+ "5 3.591809 6.293603\n",
+ "6 2.836593 5.717766\n",
+ "7 2.103368 3.913067\n",
+ "8 4.850006 10.235146\n",
+ "9 2.299206 3.985440"
]
},
- "execution_count": 58,
+ "execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
@@ -2205,7 +2177,7 @@
},
{
"cell_type": "code",
- "execution_count": 59,
+ "execution_count": 57,
"id": "0d1fcbc0-244a-45f3-a459-7b49e617d8bf",
"metadata": {},
"outputs": [
@@ -2215,13 +2187,13 @@
"<Axes: xlabel='x', ylabel='y'>"
]
},
- "execution_count": 59,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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",
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",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
@@ -2236,26 +2208,26 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 58,
"id": "8304b0fb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([[2.45726844],\n",
- " [3.13532163],\n",
- " [6.17986536],\n",
- " [4.4308288 ],\n",
- " [5.1125918 ],\n",
- " [2.97420546],\n",
- " [4.79175498],\n",
- " [1.87150233],\n",
- " [4.82673247],\n",
- " [4.25724431]])"
+ "array([[5.80566022],\n",
+ " [4.50576881],\n",
+ " [8.0166413 ],\n",
+ " [4.36337898],\n",
+ " [3.53196483],\n",
+ " [3.59180888],\n",
+ " [2.83659263],\n",
+ " [2.10336819],\n",
+ " [4.85000576],\n",
+ " [2.29920602]])"
]
},
- "execution_count": 60,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
@@ -2267,36 +2239,36 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 59,
"id": "b9f4403e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([[0.0341988 , 0.04363555, 0.0860077 , 0.06166565, 0.07115402,\n",
- " 0.04139323, 0.0666888 , 0.02604646, 0.0671756 , 0.0592498 ],\n",
- " [0.04363555, 0.05567624, 0.10974048, 0.07868153, 0.0907881 ,\n",
- " 0.05281518, 0.08509076, 0.03323366, 0.08571189, 0.07559906],\n",
- " [0.0860077 , 0.10974048, 0.2163036 , 0.15508497, 0.17894759,\n",
- " 0.10410119, 0.16771787, 0.0655051 , 0.16894213, 0.14900928],\n",
- " [0.06166565, 0.07868153, 0.15508497, 0.11119254, 0.12830152,\n",
- " 0.07463829, 0.12025006, 0.04696573, 0.12112783, 0.10683641],\n",
- " [0.07115402, 0.0907881 , 0.17894759, 0.12830152, 0.14804303,\n",
- " 0.08612273, 0.1387527 , 0.05419225, 0.13976553, 0.12327512],\n",
- " [0.04139323, 0.05281518, 0.10410119, 0.07463829, 0.08612273,\n",
- " 0.05010115, 0.08071817, 0.03152587, 0.08130737, 0.07171422],\n",
- " [0.0666888 , 0.08509076, 0.16771787, 0.12025006, 0.1387527 ,\n",
- " 0.08071817, 0.13004538, 0.05079146, 0.13099465, 0.11553908],\n",
- " [0.02604646, 0.03323366, 0.0655051 , 0.04696573, 0.05419225,\n",
- " 0.03152587, 0.05079146, 0.01983748, 0.05116221, 0.04512577],\n",
- " [0.0671756 , 0.08571189, 0.16894213, 0.12112783, 0.13976553,\n",
- " 0.08130737, 0.13099465, 0.05116221, 0.13195085, 0.11638246],\n",
- " [0.0592498 , 0.07559906, 0.14900928, 0.10683641, 0.12327512,\n",
- " 0.07171422, 0.11553908, 0.04512577, 0.11638246, 0.10265093]])"
+ "array([[0.16524954, 0.12825005, 0.22818184, 0.12419713, 0.10053216,\n",
+ " 0.10223553, 0.08073942, 0.05986927, 0.13804824, 0.0654435 ],\n",
+ " [0.12825005, 0.09953477, 0.17709177, 0.09638931, 0.07802294,\n",
+ " 0.07934492, 0.0626618 , 0.0464645 , 0.10713915, 0.05079065],\n",
+ " [0.22818184, 0.17709177, 0.31508079, 0.17149537, 0.13881802,\n",
+ " 0.14117009, 0.11148757, 0.0826694 , 0.19062143, 0.09036648],\n",
+ " [0.12419713, 0.09638931, 0.17149537, 0.09334324, 0.07555728,\n",
+ " 0.07683749, 0.06068159, 0.04499614, 0.10375337, 0.04918559],\n",
+ " [0.10053216, 0.07802294, 0.13881802, 0.07555728, 0.06116032,\n",
+ " 0.06219659, 0.0491191 , 0.03642241, 0.08398382, 0.03981358],\n",
+ " [0.10223553, 0.07934492, 0.14117009, 0.07683749, 0.06219659,\n",
+ " 0.06325043, 0.04995135, 0.03703954, 0.08540681, 0.04048817],\n",
+ " [0.08073942, 0.0626618 , 0.11148757, 0.06068159, 0.0491191 ,\n",
+ " 0.04995135, 0.03944854, 0.02925158, 0.06744911, 0.0319751 ],\n",
+ " [0.05986927, 0.0464645 , 0.0826694 , 0.04499614, 0.03642241,\n",
+ " 0.03703954, 0.02925158, 0.0216904 , 0.05001434, 0.02370993],\n",
+ " [0.13804824, 0.10713915, 0.19062143, 0.10375337, 0.08398382,\n",
+ " 0.08540681, 0.06744911, 0.05001434, 0.11532449, 0.05467102],\n",
+ " [0.0654435 , 0.05079065, 0.09036648, 0.04918559, 0.03981358,\n",
+ " 0.04048817, 0.0319751 , 0.02370993, 0.05467102, 0.02591748]])"
]
},
- "execution_count": 61,
+ "execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
@@ -2308,7 +2280,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 60,
"id": "db976c33",
"metadata": {},
"outputs": [
@@ -2341,63 +2313,63 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
- " <td>2.457268</td>\n",
- " <td>6.443569</td>\n",
- " <td>5.067716</td>\n",
+ " <td>5.805660</td>\n",
+ " <td>10.829947</td>\n",
+ " <td>10.936833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
- " <td>3.135322</td>\n",
- " <td>6.148335</td>\n",
- " <td>6.466091</td>\n",
+ " <td>4.505769</td>\n",
+ " <td>7.700637</td>\n",
+ " <td>8.488068</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
- " <td>6.179865</td>\n",
- " <td>12.321621</td>\n",
- " <td>12.744967</td>\n",
+ " <td>8.016641</td>\n",
+ " <td>15.834128</td>\n",
+ " <td>15.101928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
- " <td>4.430829</td>\n",
- " <td>8.021383</td>\n",
- " <td>9.137864</td>\n",
+ " <td>4.363379</td>\n",
+ " <td>7.800512</td>\n",
+ " <td>8.219831</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
- " <td>5.112592</td>\n",
- " <td>10.218929</td>\n",
- " <td>10.543889</td>\n",
+ " <td>3.531965</td>\n",
+ " <td>5.616152</td>\n",
+ " <td>6.653594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
- " <td>2.974205</td>\n",
- " <td>6.030322</td>\n",
- " <td>6.133815</td>\n",
+ " <td>3.591809</td>\n",
+ " <td>6.293603</td>\n",
+ " <td>6.766330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
- " <td>4.791755</td>\n",
- " <td>10.157226</td>\n",
- " <td>9.882215</td>\n",
+ " <td>2.836593</td>\n",
+ " <td>5.717766</td>\n",
+ " <td>5.343637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
- " <td>1.871502</td>\n",
- " <td>3.409193</td>\n",
- " <td>3.859669</td>\n",
+ " <td>2.103368</td>\n",
+ " <td>3.913067</td>\n",
+ " <td>3.962372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
- " <td>4.826732</td>\n",
- " <td>9.876410</td>\n",
- " <td>9.954351</td>\n",
+ " <td>4.850006</td>\n",
+ " <td>10.235146</td>\n",
+ " <td>9.136549</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
- " <td>4.257244</td>\n",
- " <td>10.435702</td>\n",
- " <td>8.779874</td>\n",
+ " <td>2.299206</td>\n",
+ " <td>3.985440</td>\n",
+ " <td>4.331296</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@@ -2405,19 +2377,19 @@
],
"text/plain": [
" x y p\n",
- "0 2.457268 6.443569 5.067716\n",
- "1 3.135322 6.148335 6.466091\n",
- "2 6.179865 12.321621 12.744967\n",
- "3 4.430829 8.021383 9.137864\n",
- "4 5.112592 10.218929 10.543889\n",
- "5 2.974205 6.030322 6.133815\n",
- "6 4.791755 10.157226 9.882215\n",
- "7 1.871502 3.409193 3.859669\n",
- "8 4.826732 9.876410 9.954351\n",
- "9 4.257244 10.435702 8.779874"
+ "0 5.805660 10.829947 10.936833\n",
+ "1 4.505769 7.700637 8.488068\n",
+ "2 8.016641 15.834128 15.101928\n",
+ "3 4.363379 7.800512 8.219831\n",
+ "4 3.531965 5.616152 6.653594\n",
+ "5 3.591809 6.293603 6.766330\n",
+ "6 2.836593 5.717766 5.343637\n",
+ "7 2.103368 3.913067 3.962372\n",
+ "8 4.850006 10.235146 9.136549\n",
+ "9 2.299206 3.985440 4.331296"
]
},
- "execution_count": 62,
+ "execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
@@ -2429,7 +2401,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 61,
"id": "9aab4539",
"metadata": {},
"outputs": [
@@ -2439,13 +2411,13 @@
"<Axes: xlabel='x', ylabel='p'>"
]
},
- "execution_count": 63,
+ "execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
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",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
@@ -2472,7 +2444,7 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 62,
"id": "bfd1ce1d",
"metadata": {},
"outputs": [
@@ -2522,7 +2494,7 @@
"y 8 12"
]
},
- "execution_count": 64,
+ "execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
@@ -2537,7 +2509,7 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": 63,
"id": "cae18757",
"metadata": {},
"outputs": [
@@ -2547,7 +2519,7 @@
"5.0"
]
},
- "execution_count": 65,
+ "execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
@@ -2559,17 +2531,17 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 64,
"id": "1e096c19",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "2.561718483508864"
+ "2.0278336399647197"
]
},
- "execution_count": 66,
+ "execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
@@ -2590,7 +2562,7 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 65,
"id": "6758c077",
"metadata": {
"scrolled": true
@@ -2599,10 +2571,10 @@
{
"data": {
"text/plain": [
- "<matplotlib.image.AxesImage at 0x7f195d19fcd0>"
+ "<matplotlib.image.AxesImage at 0x7f22571371c0>"
]
},
- "execution_count": 67,
+ "execution_count": 65,
"metadata": {},
"output_type": "execute_result"
},
diff --git a/lecture_material/21-linalg-2/21-linalg2_001.ipynb b/lecture_material/21-linalg-2/21-linalg2_001.ipynb
index a07758cbeb2b3446fd4d67df38d958271810dc19..e8d2888292b9942809e3f20625c25e40efb72c1a 100644
--- a/lecture_material/21-linalg-2/21-linalg2_001.ipynb
+++ b/lecture_material/21-linalg-2/21-linalg2_001.ipynb
@@ -317,7 +317,7 @@
"* $c$ (known) is a vector of coefficients (our model parameters)\n",
"* $y$ (computed) are the prices\n",
"\n",
- "**Below:** what if X and y are know, and we want to find c?"
+ "**Below:** what if X and y are known, and we want to find c?"
]
},
{
diff --git a/lecture_material/21-linalg-2/21-linalg2_002.ipynb b/lecture_material/21-linalg-2/21-linalg2_002.ipynb
index a07758cbeb2b3446fd4d67df38d958271810dc19..e8d2888292b9942809e3f20625c25e40efb72c1a 100644
--- a/lecture_material/21-linalg-2/21-linalg2_002.ipynb
+++ b/lecture_material/21-linalg-2/21-linalg2_002.ipynb
@@ -317,7 +317,7 @@
"* $c$ (known) is a vector of coefficients (our model parameters)\n",
"* $y$ (computed) are the prices\n",
"\n",
- "**Below:** what if X and y are know, and we want to find c?"
+ "**Below:** what if X and y are known, and we want to find c?"
]
},
{