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mooc-rr
Commit 5547742a authored by 1899a69eca748c9e8b02007d0e49196a's avatar 1899a69eca748c9e8b02007d0e49196a
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Fix analysis

parent 599ba9ff
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Showing with 102 additions and 16 deletions
module2/exo5/exo5_en.ipynb
View file @ 5547742a
......@@ -344,7 +344,7 @@
"outputs": [
{
"data": {
"image/png": 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\n",
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -394,19 +394,85 @@
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "cannot import name 'factorial'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-2d7fe3483b37>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Success\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCount\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMalfunction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Intercept\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/statsmodels/api.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mrobust\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrobust_linear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRLM\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m from .discrete.discrete_model import (Poisson, Logit, Probit,\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mMNLogit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNegativeBinomial\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mGeneralizedPoisson\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/statsmodels/discrete/discrete_model.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml1_slsqp\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfit_l1_slsqp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgenpoisson_p\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/statsmodels/distributions/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mempirical_distribution\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mECDF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmonotone_fn_inverter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStepFunction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0medgeworth\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mExpandedNormal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdiscrete\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgenpoisson_p\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzipoisson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzigenpoisson\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzinegbin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/statsmodels/distributions/edgeworth.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpolynomial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhermite_e\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHermiteE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmisc\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrv_continuous\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecial\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mspecial\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mImportError\u001b[0m: cannot import name 'factorial'"
"name": "stderr",
"output_type": "stream",
"text": [
"/home/friese/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: DeprecationWarning: Calling Family(..) with a link class as argument is deprecated.\n",
"Use an instance of a link class instead.\n",
" \n"
]
},
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 23</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -3.9210</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Tue, 19 May 2020</td> <th> Deviance: </th> <td> 3.0144</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>15:08:31</td> <th> Pearson chi2: </th> <td> 5.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>6</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 5.0850</td> <td> 7.477</td> <td> 0.680</td> <td> 0.496</td> <td> -9.570</td> <td> 19.740</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Temperature</th> <td> -0.1156</td> <td> 0.115</td> <td> -1.004</td> <td> 0.316</td> <td> -0.341</td> <td> 0.110</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 23\n",
"Model: GLM Df Residuals: 21\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -3.9210\n",
"Date: Tue, 19 May 2020 Deviance: 3.0144\n",
"Time: 15:08:31 Pearson chi2: 5.00\n",
"No. Iterations: 6 \n",
"Covariance Type: nonrobust \n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n",
"Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n",
"===============================================================================\n",
"\"\"\""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
......@@ -443,9 +509,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
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33lLn3JCGvCa8HsvUlk41Px3CgR7ASKAj8F8z6+uc23fEi5ybAkwByMjIcCNHjmxIrUElKyuLn313JB26b+YXb63iXztb8aerTycsRMI+KyuLUN1+odw2UPtaovoEfS7Qqdp0R6DmaF65wELnXDnwtZnl4A3+bL9UGcSuH9aFvQfKeHTeOpJiInjw0t6YhUbYi0hwqE8ffTbQw8y6mVkkcC0wp8Yys4HzAMwsFegJfOXPQoPZneedyi0juvHiZ5t4KmtjoMsRkRamzj1651yFmd0FzMXb/z7NObfazH4DLHHOzfE9d5GZrQEqgfuccxr8xcfM+OX/nMbeg2VMnptD67hIrs3sHOiyRKSFqE/XDc65d4F3a8z7VbXHDvix70tqERZm/PGq/uw5UMb/vbWSVnGRjOrTLtBliUgLoCtjT6IITxhP3zCI/h2Tufu1ZSz+ek+gSxKRFkBBf5LFRobzwk1D6dgqhonTs/lyuwZBE5GmpaAPgFZxkbx0SyYxkR4mTFtM7t6DgS5JREKYgj5AOraKZfotmRwsq2TCtMXsPVAW6JJEJEQp6AOoV7tEnh8/hK17i7lVQyWISBNR0AfYsO6t+evYASzbuo+7X1tGRaWGNxYR/1LQNwMX92vPr8f04YO1O3jg7dXUNf6QiEhD1Os8eml648/oyvb9JTyVtZF2idH86IIegS5JREKEgr4ZuW9UBjsKSvnLB+tomxilq2dFxC8U9M2ImfH77/VjZ1Ep//fWStISojj/tLaBLktEgpz66JuZCE8YT18/iD7pSdz56ucs27I30CWJSJBT0DdDcVHhTLtpKGkJUdw6fQlf7zoQ6JJEJIgp6JuptIQoXrplGAATpi3WjcZFpNEU9M1Yt9Q4pk4YQn5hCbdOz+ZAaUWgSxKRIKSgb+YGdm7Fk9cNYlXefu569XNdUCUiDaagDwLnn9aWhy/vx/ycnfzirVW6oEpEGkSnVwaJ64Z1Zvv+Yv720QbaJUXzvxf2DHRJIhIkFPRB5H8v7Mk3+0v464fraZ8UrQuqRKReFPRBxMz4f1f2I7+wlF/MXkWbxCi+00sXVInI8amPPshEeMJ46vpB9G6fyJ2vLGP51n2BLklEmjkFfRA6dEFVakIkt7yYzSZdUCUix6GgD1JpCVFMvzkT5xzjdUGViByHgj6IdU+LZ+pNQ3VBlYgcl4I+yA3q3IonxnkvqPrhK59TrguqRKQGBX0IuKB3Wx65oh//WbeTSbNW6oIqETmCTq8MEeMyO7Pdd45928Qo7hvVK9AliUgzoaAPIfde0IP8whKenL+RtonRjD+ja6BLEpFmQEEfQsyM317Wl52FZTw4ZzWp8VFc0q99oMsSkQBTH32ICfeE8fi4gQzslMy9byxn4Ve7A12SiASYgj4ExUR6mDphKJ1TYvn+S0v4cntBoEsSkQBS0IeoVnGRTL8lk7jIcCZMW0zu3oOBLklEAkRBH8I6JMcw/ZZMissqGT9tMXsOlAW6JBEJAAV9iMtol8DUm4aSt7eYm19YrKtnRVogBX0LMLRrCk9cN4hV2wq44+WllFXo6lmRlkRB30Jc2Lstv7uyH/9dv4uf/P0Lqqp09axIS1GvoDez0WaWY2YbzOz+4yx3lZk5MxvivxLFX64Z0on7L+7FO19s46F3VmuoBJEWos4LpszMAzwJXAjkAtlmNsc5t6bGcgnAPcCipihU/OOOc09hz4Eypnz8FSlxkdx7ge49KxLq6rNHnwlscM595ZwrA14HLqtlud8CfwRK/FifNIFJF/fiqsEdeeyD9Uz/bFOgyxGRJlafIRA6AFurTecCw6ovYGYDgU7OuX+a2U+PtSIzuw24DSAtLY2srKwGFxwsioqKmnX7Lm7t2NjGw4NzVpO3aQNnpjdsNIzm3r4TEcptA7WvJarPX7fVMu9w566ZhQF/AW6qa0XOuSnAFICMjAw3cuTIehUZjLKysmju7RtxdiU3v5DN1FV7yBzQjwt61/9G48HQvsYK5baB2tcS1afrJhfoVG26I7Ct2nQC0BfIMrNNwHBgjg7INn/RER6emzCEvumJ/PDVz1mwUePiiISi+gR9NtDDzLqZWSRwLTDn0JPOuf3OuVTnXFfnXFdgITDGObekSSoWv4qPCueFmzPpkhLLxOnZLN+6L9AliYif1Rn0zrkK4C5gLrAWmOmcW21mvzGzMU1doDS9lLhIXp44jNbxUUyYtpic7YWBLklE/Khe59E75951zvV0zp3inHvEN+9Xzrk5tSw7UnvzwadtYjSvTBxGdEQYN0xdxNe7DgS6JBHxE10ZK4d1SonllYnDqKxyXP/cQo14KRIiFPRyhFPbJDDj1kyKSiu4/vlF7CjQZREiwU5BL0fpk57Ei7dksquwlOufX8SuotJAlyQiJ0BBL7Ua1LkVU28aSu7eg9zw/CL2aix7kaCloJdjGt69Nc+PH8pXuw5ww9RF7D9YHuiSRKQRFPRyXGf1SOXZGwezfkcR46ctYn/xiYX97GV5jPj9R3S7/1+M+P1HzF6W56dKpbnRtm4+FPRSp/My2vDU9YNY800BE6YtpqCkcWE/e1kek2atJG9fMQ7I21fMpFkrFQAhSNu6eVHQS71c0LstT143iFV5+5kwbTHFFQ0fy37y3ByKyyuPmFdcXsnkuTn+KlOaCW3r5kVBL/V2UZ92PHHdIFbm7ufR7JIG79lv21fcoPkSvLStmxcFvTTI6L7esN9UUMX4qQ3rxklPjmnQfAle2tbNi4JeGmx033bcOSCK1dv2c+PUxfU+QHvfqAxiIjxHzIuJ8HDfqIymKFMCSNu6eVHQS6MMahvO09cPZu22Aq5/fmG9zrO/fGAHfndlPzokx2BAh+QYfndlPy4f2KHpC5aTStu6eWnYbYVEqrmgd1umjB/MbTOWMu65hbw8cRip8VHHfc3lAzvoj72F0LZuPrRHLydkZEYbXrhpKJt2H2DsswvYvl9j44g0Nwp6OWEjTk1l+s2Z7Cgo5epnP2PrHo16KdKcKOjFL4Z1b80rE4dRUFzBVc98xoZ83bxEpLlQ0IvfnN4pmTduH05lFVzz7EJW5u4PdEkigoJe/KxXu0T+fscZxER4GPfcQt1wXKQZUNCL33VLjeMfPziT9knRTHhhMfNWbw90SSItmoJemkS7pGhm3n4Gp7VP5I6Xl/La4i2BLkmkxVLQS5NpFRfJqxOHcXaPNCbNWsnfPlyPcw0fDE1EToyCXppUXFQ4z08YwpUDO/Dn99fxi9mrqKisCnRZIi2KroyVJhfhCePRq0+nTWI0z/xnIzv2l/D4dQOJjdSvn8jJoD16OSnCwoz7L+7Fby/rw/ycfMZNWUh+oa6iFTkZFPRyUt14RleevXEI63YUccWTn7Fuhy6sEmlqCno56S7s3ZaZt59BWWUV33vqM/67fmegSxIJaQp6CYh+HZOYfecIOrSK4aYXspmxYFOgSxIJWQp6CZgOyTG8+YMzGdkzjQfeXs0Ds1dRrjNyRPxOQS8BFR8VzpTxQ7j9nO7MWLiZCdMW1+smJiJSfwp6CThPmDHpktN49OrTWbJ5L5c+8QlrthUEuiyRkKGgl2bjqsEdmXn7GVRUOr739Ge888W2QJckEhIU9NKsDOiUzJy7R9AnPZG7X1vGb/+5Rv32IidIQS/NTpuEaF79/nBuOrMrUz/5muufW0R+gS6uEmksBb00S5HhYTw0pg9/vXYAK/P2c8nfPuGzDbsCXZZIUKpX0JvZaDPLMbMNZnZ/Lc//2MzWmNkKM/vQzLr4v1RpiS4b0IG37xpBcmwEN0xdxN8+XE9llUbAFGmIOoPezDzAk8DFQG9gnJn1rrHYMmCIc64/8CbwR38XKi1Xz7YJvH3nCC4b4B0B84bnF7FDXTki9VafPfpMYINz7ivnXBnwOnBZ9QWcc/Odcwd9kwuBjv4tU1q6uKhw/nzN6fzxqv4s37qPi//6Xz76ckegyxIJClbXjSDM7CpgtHNuom/6RmCYc+6uYyz/BLDdOfdwLc/dBtwGkJaWNnjmzJknWH7zVVRURHx8fKDLaDKBbN+2oiqe/qKUrYVVnN85nLEZkUR6zG/r17YLbqHevvPOO2+pc25IQ15TnwHBa/sLqvXTwcxuAIYA59b2vHNuCjAFICMjw40cObJ+VQahrKws1L6mc+WoSv74Xg7TPv2aLSXRPHbtAPqkJ/ll3YFuW1NT+1qe+nTd5AKdqk13BI66ksXMLgB+AYxxzpX6pzyR2kVHePjVpb156ZZM9heXc/mTn/LER+t19yqRWtQn6LOBHmbWzcwigWuBOdUXMLOBwLN4Qz7f/2WK1O6cnmnMvfccRvVpx6Pz1vG9ZxawIb8o0GWJNCt1Br1zrgK4C5gLrAVmOudWm9lvzGyMb7HJQDzwdzNbbmZzjrE6Eb9rFRfJE9cN4vFxA9m8+wCX/O2/PJ21UXv3Ij71ummnc+5d4N0a835V7fEFfq5LpMEqqxzR4R72HSznD+99yauLNnPt0M68ungL2/YVk54cw32jMrh8YIcmq2H2sjwmz805ae/XEL+cvZLXFm3l3r7l3DrpXcYN68TDl/cLdFlyEujuzBISZi/LY9KslRSXVx6et3VvMZPn5RyezttXzKRZKwGaJHxr1tDU79cQv5y9kpcXbjk8Xenc4WmFfejTEAgSEibPzTki5I+luLySyXNz6lzOXzU05fs1xGuLtjZovoQWBb2EhG37iuu9bF4DlvVHDQ2pralUHuN6mWPNl9CioJeQkJ4cU+9lDXhy/gZK6vEfgD9qaEhtTcVjtV9Qdqz5EloU9BIS7huVQUyE54h5EWFGRI0rZqPCw+jXMYnJc3O46C8f896q7dR1dfiJ1BAT4eG+URl+Wf+JGDesU4PmS2jRwVgJCYcOdtY846W2eZcP7MCnG3bx63dWc8fLSxnWLYUHvtubvh1O7MraY9UQ6AOx8O0B10N98h4znXXTgtQ51k1TycjIcDk5gT9I1VRC/TLsUGhfRWUVr2dv5S/vr2PPwTIuH9CBH1/Yk40rFgd9244nFLbd8YR6+8ysSca6EQlJ4Z4wbhjehTED0nk6ayPTPvmaf634hvM6htFvSCmt46MCXaKIX6iPXlq8xOgIfj66F1n3jeTKQR2Yt7mCs/84n0fn5rC/uDzQ5YmcMO3Ri/i0T4rh99/rT/+oXXxWkMwT8zcwfcEmbhnRjVvO6kZSTESgSxRpFO3Ri9SQHh/GE9cN4t17zubMU1rz1w/Xc9bvP+JP83LYc6As0OWJNJiCXuQYeqcn8uyNQ3j3nrM5q0cqj3+0gRG//4hfv7O6WVwEJVJf6roRqUPv9ESevmEw63cU8vR/NvLSgs3MWLCZ7/Zvz/fP6e63G56INBUFvUg99WibwJ+vGcCPL+zJC59u4vXFW5i9fBtndG/NzSO6cv5pbfGE6UpTaX4U9CIN1LFVLA98tzf3nN+D1xZv4aXPNnHbjKV0SonhhmFduGZIJ1rFRQa6TJHD1Ecv0khJMRHcce4pfPyz83jq+kG0T4rhd//+kmG/+5Afz1zOkk17/Da8gsiJ0B69yAkK94RxSb/2XNKvPTnbC5mxcBOzl21j1ud5nNomnmuHduLygR1I1QVYEiDaoxfxo4x2CTx8eT8W/d/5/OF7/YiPCufhf61l+P/7kO+/tIT3Vn1DaYV/R80UqYv26EWaQFxUOGOHdmbs0M6s21HIP5bmMmtZHu+v2UFSTAT/0789Y05PZ2jXFB3AlSanoBdpYj3bJjDpktO4b1QGn27czexlebz1eR6vLtpC28QoLunXnv/p155BnVsRptCXJqCgFzlJwj1hnNszjXN7pvHIFRV8uDafd77YxisLt/DCp5tokxDFqD7tGNWnHcO6pxDhUc+q+IeCXiQAYiPDufT0dC49PZ3CknI++jKfd1d+w9+XbmXGws0kRoczMqMN55/WhnN7ppEcq9M1pfEU9CIBlhAdwWUDOnDZgA4Ul1XyyYZdzFu9nfk5+cz5YhthBoM6t/L+N5CRRt/0JHXxSIMo6EWakZhIDxf2bsuFvdtSVeX4IncfH32Zz3/W7eRP76/jT++vo1VsBGeemspZp6ZyRvfWdGkdi+ner3IcCnqRZioszBjYuRUDO7fiJ2DVNmMAAA2wSURBVBdlsKuolE/W7+K/63fxyYad/GvFNwC0T4rmjO6tyeyWQma3FLqlxin45QgKepEgkRofxeUDO3D5wA4459i48wALvtrNgo27+M+6ncxalnd4ucFdkhnSJYVBXZLpk55EdI2blkvLoqAXCUJmxqlt4jm1TTw3Du9yOPgXfb2bpZv2smTzXuau3gFAhMfo3T6R/h2T6d8xibLCKiqrnM7fb0EU9CIhoHrwXz+sCwD5BSUs27qP5Vv3sWzLXmZ9nsuMhZsBeHjxXE5rn0DfDkn0bp9I7/REerZN0J5/iFLQi4SoNonRh8/LB6iscny1s4iZHyykIjGdVXn7+cfSXF4q8w7JEGbQNTWOXu0S6NEmgYx2CfRsG0+X1nE6pz/IKehFWghPmNGjbQIjOkQwcmQfAKqqHFv3HmT1tgK+3F7Il98UsHpbAf9etZ1DA2+GhxldWsdySlo83dPi6Z4aR7e0OLq2jiM1PlIHfoOAgl6kBQsLM7q0jqNL6zgu6df+8Pzisko27ixi3Y5CNu4sYkN+ERt3HmB+Tj7lld8OvRwfFU7nlFi6tI6lc0osnVJi6dgqhk4psXRIjlFXUDOhoBeRo8REeujbIYm+HY68TWJFZRW5e4v5evcBNu3yfm3ec5CcHYV8uDafssqqI5ZPjY+kQ3IM6ckxtE+KIT05mnZJ0bRPiqZtYjRtEqKJDFe3UFNT0ItIvYV7wuiaGkfX1DjIOPK5qipHfmEpW/ceZOueg+TtLSZvn/dr3Y5CsnJ2Ulx+9BDNKXGRtEmIIi0hijYJ0aT5HqfGR5IaH0VqfBQpcZG0io0gXMcKGkVBLyJ+ERZmtEvy7rEP7Zpy1PPOOQqKK/imoJhv9peQX1DCjoJStheUkF9Qys7CEjbkF7GrqPSI7qFDzLx39UqJiyQlNpJWvvBvFRtJcmwkybERJMVEsGl3Jal5+0mKiSApNoL4yPAWP2SEgl5ETgozIynWG7692iUec7lDHwg7i0rYVVTGrqJSdheVsftAGXsOlLL3QDl7DpSxdc9BVuSWsfdgOWUVR3YZ/TH7k2rv6z2WkBgdQUJ0uO8rgviocOKjw0mICic+Kpy4qHDiojze75He6dhID7GR3nkxkR5iIzxB+V9FvYLezEYDfwU8wPPOud/XeD4KeAkYDOwGxjrnNvm3VJGWYfayPCbPzWHbvmLSk2O4b1QGlw/swPXPLeDTjXsOLzfilBSuHtK51mVrW8eSzXt4bdFW7u1bzq2T3mXcsE48fHm/BtVwrPkNWccvZ6/ktUVbqXQOj9lRdVT/QFiVd/Q6gCPm/eHKfozq2479xeXsO1hO1mfZdMvoQ0FxOQUl5WRv2sMn63eRt6+Y6PAwOqbEUlxeycadFRSVVFBUWkFpjQ+K44nwGDERHmIiPcREeIj2PY4O9xAdEUa0b150RBhR4R6iIsKI9n2PCvcQGR5GlO8r0hPmm/bOjzw8z4jwPRfhCSMiLIwI37zGqDPozcwDPAlcCOQC2WY2xzm3ptpitwJ7nXOnmtm1wB+AsY2qSKQFm70sj0mzVh7uy87bV8ykWSt5cv561ucfOGLZTzfuOSL4Dy27ZPMe/rE074h1/PiN5VSPskrneHnhFoCjwv5YNdS23kmzVgIcFfbHWsffl2w5ouaG1nHf378A43DXTt6+Yv7vrVWYGZcP7ED7pBh2tPYwsm+7w+v4eN2uw+soqagib28xv7uy3xE1l1dWcaC0ggNlld7vpRUcKK3kQFkFxWWVHCyr5GBZhe97JcVlFZSUV3GwvJIS31dxWSWFpeWUlFdRUl5JaYXve3nVUQepT7b67NFnAhucc18BmNnrwGVA9aC/DHjI9/hN4AkzM+fc0R1tInJMk+fmHHXAsri88qiQP5bi8srDe8vVHStmXlu09aiAPVYNta23uLySyXNzjgr6Y62jesg3po7yqqMj5Vg1HK+OmstHeMJ8/fy1lnfCqqocZZVVlJZXUVr5bfiXVVRRWlFFue9xWcW388sqqqioqqKs0nkfV3qXu/sPDX9/qyuLzewqYLRzbqJv+kZgmHPurmrLrPItk+ub3uhbZleNdd0G3Oab7AusanjJQSMV2FXnUsErlNsXsLZFtjt1cFO/R+XB/Xhivz1tsmz7hqUnWkNzWEe11x/efsdbR833CyIZzrmEhrygPnv0tR2urvnpUJ9lcM5NAaYAmNkS59yQerx/UFL7glcotw287avYnx/S7Qv17dfQ19SnZz8X6FRtuiOw7VjLmFk4kATU/j+aiIicVPUJ+mygh5l1M7NI4FpgTo1l5gATfI+vAj5S/7yISPNQZ9eNc67CzO4C5uI9vXKac261mf0GWOKcmwNMBWaY2Qa8e/LX1uO9p5xA3cFA7Qteodw2UPuCXYPbV+fBWBERCW7Bd4mXiIg0iIJeRCTEnZSgN7NoM1tsZl+Y2Woz+7VvfjczW2Rm683sDd/B3qBkZh4zW2Zm//RNh1LbNpnZSjNbfujULjNLMbP3fe1738xaBbrOxjKzZDN708y+NLO1ZnZGqLTPzDJ82+3QV4GZ3RtC7ftfX6asMrPXfFkTSn97P/K1bbWZ3eub1+Btd7L26EuB7zjnTgcGAKPNbDjeoRL+4pzrAezFO5RCsPoRsLbadCi1DeA859yAaucn3w986Gvfh77pYPVX4D3nXC/gdLzbMSTa55zL8W23AXjHojoIvEUItM/MOgD3AEOcc33xnixyaAiWoP/bM7O+wPfxjk5wOvBdM+tBY7adc+6kfgGxwOfAMLxXr4X75p8BzD3Z9fipTR19P/DvAP/EewFZSLTNV/8mILXGvBygve9xeyAn0HU2sm2JwNf4TkwItfbVaNNFwKeh0j6gA7AVSMF7BuE/gVGh8rcHXI13EMlD0w8AP2vMtjtpffS+ro3lQD7wPrAR2Oecq/Atkot3wwWjx/BugENDirQmdNoG3quc55nZUt8wFgBtnXPfAPi+twlYdSemO7ATeMHX9fa8mcUROu2r7lrgNd/joG+fcy4PeBTYAnwD7AeWEjp/e6uAc8ystZnFApfgvTC1wdvupAW9c67Sef997Ij3X5HTalvsZNXjL2b2XSDfOVd93Ix6DQkRREY45wYBFwN3mtk5gS7Ij8KBQcDTzrmBwAGCsBujLr5+6jHA3wNdi7/4+qYvA7oB6UAc3t/RmoLyb885txZvN9T7wHvAF0DFcV90DCf9rBvn3D4gCxgOJPuGTIDah1YIBiOAMWa2CXgdb/fNY4RG2wBwzm3zfc/H27+bCewws/YAvu/5gavwhOQCuc65Rb7pN/EGf6i075CLgc+dczt806HQvguAr51zO51z5cAs4ExC629vqnNukHPuHLwXo66nEdvuZJ11k2Zmyb7HMXg30FpgPt4hE8A7hMLbJ6Mef3LOTXLOdXTOdcX7r/FHzrnrCYG2AZhZnJklHHqMt593FUcOexG07XPObQe2mtmhO6Cej3cI7pBoXzXj+LbbBkKjfVuA4WYWa2bGt9suJP72AMysje97Z+BKvNuwwdvupFwZa2b9gel4j4qHATOdc78xs+5494JTgGXADc650iYvqImY2Ujgp86574ZK23zteMs3GQ686px7xMxaAzOBznj/4K52zgXlQHZmNgB4HogEvgJuxvd7Smi0LxbvQcvuzrn9vnkhsf18p2qPxdulsQyYiLdPPuj/9gDM7L94j/mVAz92zn3YmG2nIRBEREKcrowVEQlxCnoRkRCnoBcRCXEKehGREKegFxEJcfW5ObjISeU7fexD32Q7oBLvMAUAmc65soAUdhxmdgvwru+8fJFmRadXSrNmZg8BRc65R5tBLR7nXOUxnvsEuMs5t7wB6wuvNiaLSJNR140EFTObYN57Gyw3s6fMLMzMws1sn5lNNrPPzWyumQ0zs/+Y2VdmdonvtRPN7C3f8zlm9st6rvdhM1sMZJrZr80s2zdG+DPmNRbv8Ntv+F4faWa51a4GH25mH/geP2xmz5rZ+3gHUgs3sz/73nuFmU08+T9VCXUKegkavvG5rwDO9A2QF863N6JPAub5Bl8rAx7Ce0n81cBvqq0m0/eaQcB1ZjagHuv93DmX6ZxbAPzVOTcU6Od7brRz7g1gOTDWecd+r6traSBwqXPuRuA2vIPiZQJD8Q4a17kxPx+RY1EfvQSTC/CG4RLv0CbE4L20H6DYOfe+7/FKYL9zrsLMVgJdq61jrnNuL4CZzQbOwvt3cKz1lvHtEBAA55vZfUA0kIp3WNx/N7AdbzvnSnyPLwJOM7PqHyw98F7aLuIXCnoJJgZMc849cMRM70iF1feiq/De1ezQ4+q/5zUPSrk61lvsfAeyfGPGPAEMcs7lmdnDeAO/NhV8+x9zzWUO1GjTD51zHyLSRNR1I8HkA+AaM0sF79k5jejmuMi894iNxTuW+acNWG8M3g+OXb4RPb9X7blCIKHa9Ca8t+6jxnI1zQV+eGhYXfPe4zWmgW0SOS7t0UvQcM6t9I1W+IGZheEd0e8OGjbe+CfAq8ApwIxDZ8nUZ73Oud1mNh3vMM2bgUXVnn4BeN7MivEeB3gIeM7MtgOLj1PPs3hHIVzu6zbKx/sBJOI3Or1SWgzfGS19nXP3BroWkZNJXTciIiFOe/QiIiFOe/QiIiFOQS8iEuIU9CIiIU5BLyIS4hT0IiIh7v8Dh/QV83uje2AAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
......@@ -517,6 +596,13 @@
"### My Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"proba that it fails at 30 F is > 90 %"
]
},
{
"cell_type": "code",
"execution_count": null,
......@@ -542,7 +628,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
"version": "3.7.3"
}
},
"nbformat": 4,
......
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