update-question-sujet-6

parent db93faf6
...@@ -52,7 +52,7 @@ ...@@ -52,7 +52,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 71, "execution_count": 109,
"metadata": { "metadata": {
"hideCode": false, "hideCode": false,
"hidePrompt": false "hidePrompt": false
...@@ -64,6 +64,7 @@ ...@@ -64,6 +64,7 @@
"import seaborn as sns # Pour la visualisation\n", "import seaborn as sns # Pour la visualisation\n",
"import pandas as pd\n", "import pandas as pd\n",
"import isoweek\n", "import isoweek\n",
"import numpy as np\n",
"import statsmodels.api as sm # Pour la régression logistique" "import statsmodels.api as sm # Pour la régression logistique"
] ]
}, },
...@@ -432,6 +433,44 @@ ...@@ -432,6 +433,44 @@
"grouped" "grouped"
] ]
}, },
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfsAAAGDCAYAAAAs+rl+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XuYJHV97/H3h10R5SJRFi/AAhISggkoLiiRqEQlYDyCxggGxQsEiUFiDBqexOjRmBwvBxMvKCJBI9GAGjlBBUGIxigQdhXkomIWBFkXZBEEQbl/zx9Vo00zM1uzTM3s1r5fz9PPdFX9qurb3dP96fpVdVWqCkmSNFwbzHcBkiSpX4a9JEkDZ9hLkjRwhr0kSQNn2EuSNHCGvSRJA2fYa14l2SnJPfNdx2xIsm+S5SPDVybZs8N8C5KcmeSQfiv8xfo2add3a5KT52Kd7XqfneRbc7Su65PsNRfrmk8P5v2TZIMkn0jykyRfne3atHYx7NcTSW4bud2X5OcjwwfPd31royTvSHLims5fVTtU1fkdlvVu4P9V1cfXdF0z9BJgE+BXquplfawgyUZJKsnWE+Oq6pyq2rWP9c2FJKckedMM2h+R5JwZtH/Ac9azZwF7Ao+tqqfP0To1TxbOdwGaG1W1ycT9JFcDh1VV5w+i9U2SOXtvVNXr52pdrW2BK6rq3jler9Yu2wJXVdXP57uQ6SRZWFWD6P2bV1XlbT27AVcDzx4b9zTgv4FbgJXAPwAL22k7AfeMtb8AeGl7/6PAJ0amvRf4whTrXthO/zGwHHjt6LKBRwIfB64HrgXeAmwwxbLeAXwCOBW4DbgY2L6d58b2ce490n4xcAZwE/A94OVjy/pku6yfAi8F7gLubpd9Ydvu1cB32zbLgVeNLGNfYPnI8PXAXsABUyxrJo91AfA3wFXtY/sEsPno6wO8ElgBrALeMMVy3jlWy8Htst8K/AD4EXASsGmXZbev51vaum4FlgKPAS4ECri9Xc8Bkzw/vwX8F/AT4BJgv5FppwD/CJzVPtdfB7ad5n/60Lb+VcAbJp77kWW9aarXaZLn+QPtcm4BvgX8OnBU+5zd2T6eT7ft3wx8v63xMuD32/FPAu5on7vbgOvH3zft8BHAOe39Bzxnfb1/gNeM1fdXo7W0bTZq69l65Hl8L/CltsavAFsCH2xfw8uB3xqZfxvg32n+X68Cjhh7fad8Tdr6j26X+bP5/swcwm3eC/A2Dy/65GG/B7B7+2G3Q/tBckQ7bXVhv2n7gXcQTdfgDcBjplj364BLgccBi4CvjX1YnQm8H3g48FjgIkZCeWxZ7wB+Buzdfgie2tZxdDv8WuA7I+3/m+ZLzEOBJTSh/7SRZd0JPJdm99bD2nEnjq3z+TRfKAI8G/g58IR22qRhP7L88WXN5LEeQxOMj2s/hD8GfHTk9SnguHba7jSB/vhpnrcTR4ZfA3yHZktvM+DzwEe6LJvmC8hFwK+2z9uTgM0ZC4rx56edfg3wF8BDgN+jCZ3t2+mntP9Hu7XTPwN8bIrH80SasN2zfW2PowmxNQn7/YHz2+dhA+AJwJaTLacdd2D72m0AvKytY4t22v3Cc/x9M95msues5/fPeLh3CfvrgV1p3h9fownxA2k+N94NnNm2XdDW+ZfAhsCv0XwZe0aX16Rdz9L2cT6sz8/D9eXmPnsBUFUXVtXSqrq3qq4ETgSe0XHenwKH0GwR/TPNl4Trp2j+YuDYqlpZVauAd01MSLIt8HTg9VX1s6q6DngfzZeIqZxbVV+uppvvMzQf0se2w6cAOyV5WJIdaT6k/qqq7qyqZW2to/us/7Oqzqiq+2qKrs2qOr2qvl+Nc4D/pNl6n5E1eKyvBo5pn7c7aLbED0ySkTZvqao7qmopTe/DLh3LORh4d1VdU1W3An8NHNxx2Ye1dS1vn7eLquonHdb5O+3f91TV3VV1Fs0W44EjbT5VVd+sqrtpel2eOMWyXgz8W1WdX1V30mylruln2900/0M7AVVVl1fVDVM1rqpTq+q69rGfDPwQePIarruL2X7/zNSnq+pb7fvj34Fb2ufgXuBTNF/2oHlPbFRV76yqu6rqezQ9gDOp5R/ax7lW72ZYV7jPXgAk2Rk4lmZL6mE0/xtfn8EivgZc18572jTtHkfTvTjhmpH729JsTawayZkNaHoZpvKjkfs/B1ZVVY0MA2zcrnfV2AfHNTQ9ERNG65pUkucDb+KXW7IPp9ninqnOj7UN3W2AM5KMXrlqA+BR7f17q+rGkWk/ozkIr4vHcf/X4Rqa1/GR0y27rWsr4MqO6xlf5w9GXquJ9W41Mjz6hXG6x3O//6mquiXJLWtQEzRbxjsBHwa2SvIZ4I1VddtkjZMcCvwZzS4i2hq3WMN1dzHb75+ZGn+/jQ9PvEbbAtslGf3itwCYyXFCq30/qju37DXhI8A3gR2qajPgbTRd1dDsn1uQ5KEj7R8zNv/rabaKbqXpapzKdTTBNWHxyP1rabpyf6WqNm9vm1XVbjN+NA+0EliU5GFj6/7hyPD4JSDvN5xkY+DTwN/SdO1uDvwHv3yepjO+7M6PtQ3EHwK/O9J286raaCyE19RKmg/nCYtpPrhvmm6mkbp2mGxyh3UuHhs3/np0db//qSSPAB4xMv12mi9lE8b/d3+h7bF5T1U9iab3YleaMIcH/j/8Gk2X+eHAI9v/h+X88v9hsudgulq6XIK0z/dP5+epg2uB7479v25aVS+Ywbq8JOssMuw1YVOaLrnbkjwB+OORaStpDlg6uP1N+GsY2QJL8ps0W7svbW9vbnsKJvMp4M+TPDbJFsAbJyZU1fdp9mm+K8mm7e+Ad5yl30svpzkI7O1JHppkN+DlNAe6TeVHwPYj3dkPo9l/fANwX7uV/8yO67/fstbgsR4PvCPJNgBJtkzyvzque3X+FTg6yeIkmwJvBz45ttU9lROBv0/y+DSelGTztjv9FuDxU8z3X8AGSV6XZGGS5wD70HyZmqlPAS9M8pT2C+nbgftGpl8MPC/J5km2ojmWY1JJnppkSftrjNtpjk+Y+NXCj8Yezybtela1j+UImh4fRtpvk+QhY7W8qP2Z3U7AKyYmdHjOJh5rX++fi4EnJXlCkofTHHy4pr4G0L6+G7Wv8S7t+25iXZ1eE80Ow14T/hw4LMltNAc4nToxod0fdxi/PMp9G+AbAEk2BP4FeGtVfbuqvk3TK3Dy2IfchA/QfNBfTnPA3KfGpr+E5gCv79JsWZ4KPPrBPrg2uF4M7EzTPXwqzVHl03XBn0Kz9XFTkvPareijgc/RHA19AM3R/V3cb1ntuJk81nfRdIH+R5KfAufR7HKZDR8CPtsu88q2lq4/B3wH8AWaHo5bab6UTPQAvRn4dHvSluePztQed/A84EU0z+V7gAPb40VmpKouojnQ7zM0vxj4Ac3/6YSTaL7s/YDm4MN/nWZxm9Mc/PgTmoPPrqHZ7w1wArB7+3hOqapvto93Gc0W9/bt/QlfpDkY9oYkK9px76LZRbaqXd6/jK1/yues1dv7p6oubev7r3b+r3SZb4pl3U1zsOtv0zyHq2j+zya6+WfymmgWpNuXd0mStK5yy16SpIEz7CVJGjjDXpKkgTPsJUkaOMNekqSBG9QZ9LbYYovabrvt5rsMSZLmxDe+8Y0bq2rR6toNKuy32247li1btvqGkiQNQJJrVt/KbnxJkgbPsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgZuUFe968tfnPnx+S5BetCO3e+Q+S5B0jxxy16SpIEz7CVJGjjDXpKkgTPsJUkaOMNekqSBM+wlSRo4w16SpIEz7CVJGjjDXpKkgTPsJUkauF7DPsm+Sa5IsjzJMZNM3z/JJUkuTrIsyV5d55UkSd30FvZJFgDHAfsBOwMvSbLzWLNzgV2r6onAq4ATZzCvJEnqoM8t+z2A5VV1VVXdBZwC7D/aoKpuq6pqBzcGquu8kiSpmz7Dfivg2pHhFe24+0nygiTfBb5As3Xfed52/sPbXQDLVq1aNSuFS5I0JH2GfSYZVw8YUXVaVe0EHAD87Uzmbec/oaqWVNWSRYsWrXGxkiQNVZ9hvwLYZmR4a2DlVI2r6qvADkm2mOm8kiRpan2G/VJgxyTbJ9kQOAg4fbRBkl9Nkvb+bsCGwI+7zCtJkrpZ2NeCq+qeJEcCZwELgJOq6vIkR7TTjwf+ADgkyd3Az4ED2wP2Jp23r1olSRqy3sIeoKrOAM4YG3f8yP13Au/sOq8kSZo5z6AnSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDVyvYZ9k3yRXJFme5JhJph+c5JL2dl6SXUemXZ3k0iQXJ1nWZ52SJA3Zwr4WnGQBcBzwHGAFsDTJ6VX17ZFm3weeUVU3J9kPOAF4ysj0vavqxr5qlCRpfdDnlv0ewPKquqqq7gJOAfYfbVBV51XVze3gBcDWPdYjSdJ6qc+w3wq4dmR4RTtuKocCZ44MF3B2km8kObyH+iRJWi/01o0PZJJxNWnDZG+asN9rZPTTqmplki2BLyX5blV9dZJ5DwcOB1i8ePGDr1qSpIHpc8t+BbDNyPDWwMrxRkl2AU4E9q+qH0+Mr6qV7d8bgNNodgs8QFWdUFVLqmrJokWLZrF8SZKGoc+wXwrsmGT7JBsCBwGnjzZIshj4LPCyqvreyPiNk2w6cR/YB7isx1olSRqs3rrxq+qeJEcCZwELgJOq6vIkR7TTjwfeDDwK+GASgHuqagnwaOC0dtxC4JNV9cW+apUkacj63GdPVZ0BnDE27viR+4cBh00y31XAruPjJUnSzHkGPUmSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeB6/emdJD0YN3zojfNdgjQrtvyTd83r+t2ylyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeA6h32Sh/ZZiCRJ6sekYZ9k4cj9PZJcCvxPO7xrkvfPUX2SJOlBmmrL/tVJ9mzvvw94HvBjgKr6FrD3HNQmSZJmwVRh/2Hg4Ik2VXXN2PR7+ytJkiTNpoWTjayqe4Aj28Frk+wBVJIFwGuB781RfZIk6UHqcoDenwCvBxYDPwKeChzRZeFJ9k1yRZLlSY6ZZPrBSS5pb+cl2bXrvJIkqZtJt+zHPL6qDhodkeSptPvwp9L2AhwHPAdYASxNcnpVfXuk2feBZ1TVzUn2A04AntJxXkmS1EGXLfsPTjLuuA7z7QEsr6qrquou4BRg/9EGVXVeVd3cDl4AbN11XkmS1M2UW/btfvo9gUVJjhqZtBnwkA7L3gq4dmR4BfCUadofCpw503mTHA4cDrB48eIOZUmStH6Zrht/Y2CLts2ikfE/Bf6ww7IzybiatGGyN03Y7zXTeavqBJruf5YsWTJpG0mS1mdThn1VfRn4cpKPVtVVa7DsFcA2I8NbAyvHGyXZBTgR2K+qfjyTeSVJ0upN141/bFX9BXBskgdsMVfVC1ez7KXAjkm2B34IHAT80dg6FgOfBV5WVd+bybySJKmb6brxT23/fmBNFlxV9yQ5EjgLWACcVFWXJzminX488GbgUcAHkwDcU1VLppp3TeqQJGl9N103/oXt33PXdOFVdQZwxti440fuHwYc1nVeSZI0c9N141/EFAfFAVTVbr1UJEmSZtV03fgvmrMqJElSb6brxr9yLguRJEn9WO0Z9JLsnuSCJLckuSPJnUlunYviJEnSg9fl3PgfBF5Kc8raPYBXcP/fwEuSpLVYl3Pjb1BVVwALq+ruqvoI8Oye65IkSbOky5b97Uk2BL6V5O+B64BN+i1LkiTNli5b9q9o2x0J3AvsCPxBjzVJkqRZ1CXsn1tVd1TVT6rqb6rqKOD3+i5MkiTNji5h/6pJxh0624VIkqR+THcGvQNpLkDz+CSfHZm0GfCTvguTJEmzY7oD9C4EfkxzednjRsb/FLioz6IkSdLsme4Met9P8gPgpgdzMRxJkjS/pt1nX1X3Ancl2WyO6pEkSbOsy+/sb6P5jf3ZwO0TI6vq9b1VJUmSZk2XsD+nvUmSpHXQasO+qv4pyULgV9tRy6vqnn7LkiRJs2W1YZ/kd4CTgR8CAR6T5GVV9fW+i5MkSQ9el278f6A5i963AZL8Bk34L+mzMEmSNDu6nEFvw4mgB6iq7wAb9leSJEmaTV227L+Z5MM0W/MAB+NJdSRJWmd0CfsjgKOAN9Lss/8q8P4+i5IkSbOny9H4dwDvam+SJGkds9p99kn2TbI0yQ1Jbkpyc5Kb5qI4SZL04HXpxv8A8GLgUuC+fsuRJEmzrUvYrwAuriqDXpKkdVCXsH8j8LkkXwHunBhZVe/rqyhJkjR7uoT9W4G7gc2xG1+SpHVOl7Dfsqqe3HslkiSpF13OoHdukt/tvRJJktSLLmH/x8A5SW7zp3eSJK17unTjb9F7FZIkqTddzqB371wUIkmS+tGlG1+SJK3DDHtJkgauU9gneWqSQ9r7j0qyuN+yJEnSbFntPvskbwKeBuwAfBzYCPgksFe/pUmSpNnQZcv+RcBzgdsBquqHwGZ9FiVJkmZPl7C/s6oKKIAkD++3JEmSNJu6hP1nkxwHPCLJK4GzgZP6LUuSJM2W1YZ9Vb0T+DxwOrAr8HdV9Y9dFp5k3yRXJFme5JhJpu+U5PwkdyY5emza1UkuTXJxkmXdHo4kSRrX5Qx6VNWZwJkzWXCSBcBxwHOAFcDSJKdX1bdHmt0EHAUcMMVi9q6qG2eyXkmSdH9Thn2Sm2n300+mqh65mmXvASyvqqva5Z0C7A/8Iuyr6gbghiS/P5OiJUlSd9Nt2W8BBHgLsAo4uR0+GOhykN5WwLUjwyuAp8ygtgLOTlLAh6vqhBnMK0mSWlOG/cQ58ZPsU1WjIf3+JBcA71zNsjPZYmdQ29OqamWSLYEvJfluVX31AStJDgcOB1i82HP9SJI0rsvR+JXkwCQBSHJgx2WvALYZGd4aWNm1sKpa2f69ATiNZrfAZO1OqKolVbVk0aJFXRcvSdJ6o0vY/xFwCPDjJDcCL6Ppyl+dpcCOSbZPsiFwEM0R/auVZOMkm07cB/YBLusyryRJur8ul7i9CpjxAXRVdU+SI4GzgAXASVV1eZIj2unHJ3kMsIzmjHz3JXkdsDPN8QKntZ0JC4FPVtUXZ1qDJEnq+NO7NVVVZwBnjI07fuT+9TTd++NupflNvyRJepC8xK0kSQNn2EuSNHCrDfskH504WK4d3jrJ2f2WJUmSZkuXLftlwIVJ9mkvhPNl4EP9liVJkmZLl6Pxj0vyLZqQvxHYraqu670ySZI0K7p047+E5pK2rwL+Bfhckt/suzBJkjQ7uvz07mDg6e3P5EiyJ/AJ/GmcJEnrhC7d+M8bGz4/yUwuaCNJkubRasM+yVRXmzt8lmuRJEk96NKNf+7I/Y2AF3D/S9dKkqS1WJdu/FNHh5OcDHypt4okSdKsWpMz6G0PbDvbhUiSpH502Wd/M1Dt4AbATcAxfRYlSZJmT5d99luM3L+vqmrKlpIkaa3TZZ/9vUkeAewAbNReY56qOq/n2iRJ0izo0o1/KPB6YCvgUmB34ALgmb1WJkmSZkWXA/ReBywBrq6q3wGeDHhufEmS1hFdwv6Oqvo5QJINq+pyYKd+y5IkSbNlym78JAur6h7guiSbA58DzkpyE/CjuSpQkiQ9ONPts7+Q5nK2z2+H/ybJs4BHAF/ovTJJkjQrpgv7jI+oqnMnayhJktZe04X9oiSvn2piVb2nh3okSdIsmy7sFwCbMMkWviRJWndMF/bXVdXb5qwSSZLUi+l+eucWvSRJAzBd2D9rzqqQJEm9mTLsq+qmuSxEkiT1Y02uZy9JktYhhr0kSQNn2EuSNHCGvSRJA2fYS5I0cIa9JEkDZ9hLkjRwhr0kSQNn2EuSNHCGvSRJA2fYS5I0cIa9JEkDZ9hLkjRwvYZ9kn2TXJFkeZJjJpm+U5Lzk9yZ5OiZzCtJkrrpLeyTLACOA/YDdgZekmTnsWY3AUcB/3cN5pUkSR30uWW/B7C8qq6qqruAU4D9RxtU1Q1VtRS4e6bzSpKkbvoM+62Aa0eGV7Tj+p5XkiSN6DPsM8m4mu15kxyeZFmSZatWrepcnCRJ64s+w34FsM3I8NbAytmet6pOqKolVbVk0aJFa1SoJElD1mfYLwV2TLJ9kg2Bg4DT52BeSZI0YmFfC66qe5IcCZwFLABOqqrLkxzRTj8+yWOAZcBmwH1JXgfsXFW3TjZvX7VKkjRkvYU9QFWdAZwxNu74kfvX03TRd5pXkiTNnGfQkyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeAMe0mSBs6wlyRp4Ax7SZIGzrCXJGngDHtJkgbOsJckaeB6Dfsk+ya5IsnyJMdMMj1J3tdOvyTJbiPTrk5yaZKLkyzrs05JkoZsYV8LTrIAOA54DrACWJrk9Kr69kiz/YAd29tTgA+1fyfsXVU39lWjJEnrgz637PcAllfVVVV1F3AKsP9Ym/2Bj1fjAmDzJI/tsSZJktY7fYb9VsC1I8Mr2nFd2xRwdpJvJDl8qpUkOTzJsiTLVq1aNQtlS5I0LH2GfSYZVzNo87Sq2o2mq/9Pkzx9spVU1QlVtaSqlixatGjNq5UkaaD6DPsVwDYjw1sDK7u2qaqJvzcAp9HsFpAkSTPUZ9gvBXZMsn2SDYGDgNPH2pwOHNIelf9U4Jaqui7Jxkk2BUiyMbAPcFmPtUqSNFi9HY1fVfckORI4C1gAnFRVlyc5op1+PHAG8FxgOfAz4JXt7I8GTksyUeMnq+qLfdUqSdKQ9Rb2AFV1Bk2gj447fuR+AX86yXxXAbv2WZskSesLz6AnSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvSdLAGfaSJA2cYS9J0sAZ9pIkDVyvYZ9k3yRXJFme5JhJpifJ+9rplyTZreu8kiSpm97CPskC4DhgP2Bn4CVJdh5rth+wY3s7HPjQDOaVJEkd9LllvwewvKquqqq7gFOA/cfa7A98vBoXAJsneWzHeSVJUgd9hv1WwLUjwyvacV3adJlXkiR1sLDHZWeScdWxTZd5mwUkh9PsAgC4LckVnSvU2mQL4Mb5LmLI3sPL57sErZ18782F17y7ryVv26VRn2G/AthmZHhrYGXHNht2mBeAqjoBOOHBFqv5lWRZVS2Z7zqk9Y3vvfVDn934S4Edk2yfZEPgIOD0sTanA4e0R+U/Fbilqq7rOK8kSeqgty37qronyZHAWcAC4KSqujzJEe3044EzgOcCy4GfAa+cbt6+apUkachSNemucGlOJTm83SUjaQ753ls/GPaSJA2cp8uVJGngDHvNqSSV5NiR4aOT/O95LEkatPYA6K8l2W9k3IuTfHE+69LcMuw11+4EXphki/kuRFofVLOv9gjgPUk2SrIx8HfAn85vZZpLhr3m2j0050X48/EJSbZNcm57UaRzkyye+/Kk4amqy4DPAX8JvIXmNOVXJnl5kguTXJzkg0k2SLIwyclJLk1yWZKj5rd6zYY+T6ojTeU44JIk7xob/wGaD6F/TvIq4H3AAXNenTRMbwW+CdwFLEnym8ALgN9uf+58As05Ta4Etqiq3wJIsvl8FazZY9hrzlXVrUk+DhwF/Hxk0p7AC9v7JwPjXwYkraGquj3JqcBtVXVnkmcDuwPLkgA8jOaaJGcBv57kvTTnQjl7vmrW7DHsNV/+kWYr46PTtPF3odLsuq+9QXMNkpOq6m/GGyXZheYS40cBf8Avrz+idZT77DUvquom4FPAoSOjz6PpRgQ4GPjaXNclrUfOAV48cbBskkclWZxkEc05WD5Ns39/t/ksUrPDLXvNp2OBI0eGjwJOSvIGYBXt6ZMlzb6qujTJW4FzkmwA3E1z1P69wD+l6dsvmoP6tI7zDHqSJA2c3fiSJA2cYS9J0sAZ9pIkDZxhL0nSwBn2kiQNnGEvrcWS/HWSy9vrBVyc5Cnt+NcleXiH+bu2OyDJzh1ruq1Lu5H2f5jkO0m+PJP5JM0ew15aSyXZE3gesFtV7QI8m+Z0pgCvA1Yb4jNodwDQKezXwKHAa6pq756W31mSBfNdgzQfDHtp7fVY4MaquhOgqm6sqpXtVcgeB3x5Yms5yYeSLGt7Ad7ajpus3S+2ypO8KMnHkvw28Hzg3W3vwQ6jRSQZUV8MAAACyklEQVTZPsn5SZYm+duxaW9ox18ysd6x6W8G9gKOT/LuJK9I8oGR6Z9P8syJ2pK8M8k3kpyTZI8kX0lyVZLnt20WtMuZWOer2/HPTPL5keV+IMkr2vtXJ3lzkq8Bfzjzl0Fa9xn20trrbGCbJN9rLz/6DICqeh+wEth7ZGv5r6tqCbAL8Iwku0zR7gGq6jzgdOANVfXEqrpyrMl7gQ9V1e7A9RMjk+wD7AjsATwReHKSp48t+23AMuDgqnrDah7vxsBXqurJwE+BtwPPobky29vaNocCt7S17A78cZLtV7NcgDuqaq+qOqVDW2lwDHtpLVVVtwFPprkIySrg1Imt1Um8OMk3gYuAJzC7XfJPA/61vX/yyPh92ttFNBc12okm/NfUXcAX2/uXAv9ZVXe397cbWechSS4G/ht4VMd1nvog6pLWeZ4bX1qLVdW9wFeAryS5FHg58LHRNu2W7dHA7lV1c5KPARtNtciR+1O1Wd18v1g18H+q6sMzWM493H8jY7SGu+uX5+++D5jYfXFfkonPqgCvraqz7ldIstc0ywW4fQY1SoPjlr20lkry60lGt1qfCFzT3v8psGl7fzOaMLslyaNpLk3KJO0AfpTkN9oLn7xgmnajvs79r0Y44SzgVUk2aevdKsmWq3lYVwNPTLJBkm1odgHMxFnAnyR5SLvOX0uyMc3zsnOShyZ5BPCsGS5XGjS37KW11ybA+5NsTrNFvJxfXlf8BODMJNdV1d5JLgIuB66iCWcmawccA3ye5qj+y9p1AJwCfKQ9qO9FY/vt/wz4ZJI/A/5tYmRVnZ3kN4DzmwukcRvwUuCGaR7T14Hv03TNX0bT/T8TJ9J06X+zvSrbKuCAqro2yaeAS4D/odm1IKnlVe8kSRo4u/ElSRo4w16SpIEz7CVJGjjDXpKkgTPsJUkaOMNekqSBM+wlSRo4w16SpIH7/1Tjcwea7gWrAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 6))\n",
"sns.barplot(data=grouped, x=grouped.index, y='Mortality Rate', palette='Set2')\n",
"\n",
"# Ajout des labels et du titre\n",
"plt.xlabel('Statut de fumeur')\n",
"plt.ylabel('Taux de mortalité')\n",
"plt.title('Taux de mortalité en fonction du statut de fumeur')\n",
"\n",
"# Affichage du graphique\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ces résultats semblent à première vue contre-intuitifs, car on pourrait s'attendre à ce que les fumeuses aient un taux de mortalité plus élevé que les non-fumeuses. On sait depuis longtemps que le tabagisme est largement reconnu comme un facteur de risque majeur pour diverses maladies graves, telles que les maladies cardiaques, les cancers et d'autres troubles respiratoires, qui peuvent entraîner un taux de mortalité plus élevé."
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
...@@ -661,6 +700,27 @@ ...@@ -661,6 +700,27 @@
"grouped_age" "grouped_age"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Le résultat est surprenant car, à première vue, on pourrait s'attendre à ce que les fumeuses aient un taux de mortalité plus élevé que les non-fumeuses à chaque tranche d'âge, étant donné que le tabagisme est un facteur de risque bien documenté pour de nombreuses maladies mortelles (comme les maladies cardiaques, le cancer, etc.). Cependant, le tableau montre que ce n'est pas toujours le cas."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Le paradoxe de Simpson survient lorsque des tendances apparentes dans plusieurs groupes sont inversées lorsqu'on regroupe ces groupes ensemble. Dans ce cas, bien que le tabagisme semble augmenter la mortalité dans certaines tranches d'âge, l'âge joue un rôle plus important dans les tranches plus âgées."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On a par exemple un effet confondant de l'âge : Les non-fumeuses âgées dans les tranches 55-64 ans et 65+ ont probablement d'autres facteurs de risque de mortalité (comme des maladies cardiovasculaires, l'hypertension, le diabète, etc.) qui contribuent à leur taux de mortalité élevé. À l'inverse, les fumeuses dans ces tranches d'âge peuvent être relativement jeunes et en meilleure santé globale, ce qui réduit l'impact apparent du tabagisme."
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
...@@ -829,11 +889,50 @@ ...@@ -829,11 +889,50 @@
"result.summary()" "result.summary()"
] ]
}, },
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Calcul des odds ratios (exp(coef)) et intervalles de confiance\n",
"coefficients = result.params\n",
"conf_int = result.conf_int()\n",
"conf_int['OR'] = np.exp(coefficients)\n",
"conf_int['OR Lower'] = np.exp(conf_int[0])\n",
"conf_int['OR Upper'] = np.exp(conf_int[1])\n",
"\n",
"# Affichage des résultats sous forme de graphique\n",
"plt.figure(figsize=(10, 6))\n",
"sns.barplot(x=conf_int.index, y=conf_int['OR'], yerr=[conf_int['OR Lower'], conf_int['OR Upper']])\n",
"plt.ylabel('Odds Ratio (OR)')\n",
"plt.title('Effet du Tabagisme et de l\\'Âge sur la Probabilité de Décès')\n",
"\n",
"# Affichage du graphique\n",
"plt.xticks(rotation=45)\n",
"plt.show()\n"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Cette section vous donnera les coefficients du modèle et vous permettra d'interpréter l'effet de l'âge et du tabagisme sur la probabilité de décès." "L'âge est un facteur significatif dans la probabilité de décès, ce qui n'est pas surprenant, car la mortalité augmente généralement avec l'âge.\n",
"\n",
"Mais les résultats de la régression logistique montrent que l'effet du tabagisme sur la probabilité de décès est positif, mais statistiquement non significatif. Le coefficient pour le tabagisme (Smoker_Yes) est de 0.2787, avec un p-value de 0.091. Cela signifie que, bien qu'il existe une tendance suggérant que les fumeuses ont une probabilité plus élevée de décéder que les non-fumeuses, cette relation n'est pas suffisamment robuste pour conclure que le tabagisme est un facteur significatif de la mortalité dans ce modèle. L'intervalle de confiance pour ce coefficient inclut 0, ce qui confirme cette absence de significativité."
] ]
}, },
{ {
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment