From f432ec01c3a0298220c59f2c8fabb30a45f19f56 Mon Sep 17 00:00:00 2001 From: 2d7370601c3ace1763fd5fd2b98d0195 <2d7370601c3ace1763fd5fd2b98d0195@app-learninglab.inria.fr> Date: Tue, 16 Jun 2020 14:57:50 +0000 Subject: [PATCH] Debut implementation regression logistique --- module3/exo3/exercice.ipynb | 96 ++++++++++++++++++++++++++++++++++++- 1 file changed, 95 insertions(+), 1 deletion(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index abb3a7c..215a292 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1476,7 +1476,101 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "On voit que pour les deux classes d'ages 18-34 et 65+, le taux de mortalité est le même pour les fumeurs et les non-fumeurs. En revanche, pour les classes d'age 35-54 et 55-64, le taux de mortalité des fumeurs est nettement plus élevé que celui des non-fumeurs" + "On va représenter graphiquement la mortalité en fonction de la tranche d'age afin de simplifier l'analyse" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Kpa8YHBmzHX5/HKq1hh4fO135iLzQRKCUclnLIk7wnylbCaxQgp+Ht6FCyWL/XCExFqYPAJ+ycP/P4FEs+x05OU0ESimXtDA8hqenbaORvy8/PtSaMsW9/rlCeqo1y1hSLDy0CEpVckygBUATgVLK5czeFsX//RJG8xpl+WFYK3y9Pf+5gjHWfMNH1llTTfo3d0ygBUQTgVLKpUzfeISXZofTtlZ5JgwNoUSxbE6DmybA1slw8/9B0L0FH2QB00SglHIZk9Ye4rV5u7ilnh/fDm6Jt2c2o4IPrrTKR9S7Azq/XPBBOoAmAqWUS/hm5QHe/WM33RpVYtyA5hTzyCYJnD4Ivw615hru+x24OW2l/muiiUApVaQZY/h06T4+W7aPns38+fi+Zni6Z3OCT0mwlY8w0H8aePv+e50iShOBUqrIMsbw7qLdfLvyIPe2rMZ79zTF3S2bcQCZmTBrBMTthcGzrIJyLkQTgVKqSMrMNIydt5PJ6w4zqG0NXu/VBLfskgDA8rdgz0K4430I7FSQYRYKmgiUUkVORqZh9Oxwpm86yvCbajH6roY5z2++Yyas/hBaDIHWIwo20EJCE4FSqkhJz8jk+V/D+D00mqe61OG/t9XLOQlEh8LvT0CNdnDnR0WyfEReaCJQShUZqemZPDN9G3/sOM4L3evzROc6Oa+ceNIqH1G8PNz3E3h45bxuEaeJQClVJCSnZfD4lK38tfskY3o04uGbcpnTPD0FZgyC86fh4cVQ0q/gAi2ENBEopZze+dR0Rvy4hTX743jr7iYMbFMz55WNgQX/haMboN8kqNKswOIsrDQRKKWcWkJyGg9P2szmw6f5sF8z7m1ZLfcNNnwL236Gjv+DxncXTJCFnCYCpZTTOnc+jSE/bGTHsXN89kBzejbzz32DA3/B4pegQQ/o9FLBBOkENBEopZzSqcQUBn+/kf0nE/l6YAu6Na58lQ0OwK/DwK8B3P2Ny5SPyAtNBEopp3MyPpmBEzZw5PR5vhvSkk71K+a+QXI8TOsP4maVjyhWKvf1XYwmAqWUU4k+e4GBEzZwIj6ZH4a1on3tCrlvkJkBM4fDqf0w5HcoG1AgcToTu14biUgZEflNRHaLSISItBORciLyp4jss30va88YlFJFx5FT57nv23XEJaTw08Otr54EAP56A/Ythjveg1od7R+kE7J3I9lnwCJjTAOgGRABvAgsM8bUBZbZniulVK4OxCZy37frSExJZ+ojbWlZs9zVN9r+K6z5BFoOg1bD7R+kk7JbIhARX6Aj8D2AMSbVGHMW6A1Mtq02GehjrxiUUkXD7uPx3P/tOtIzM5n2SFuCqpW++kbHtsLcJ6FmB6uYnIuWj8gLe14RBAKxwA8isk1EJohICaCSMSYGwPb9Knd5lFKubMexczzw3Xrc3YTpI9rRsEoe5glIOA7TB0KJinDfjy5dPiIv7JkIPIAWwNfGmOZAEtfQDCQiI0Rks4hsjo2NtVeMSqlCbMvhM/Qfv54SXh788mg76lQsefWN0pKtJJB8FvpPhRJ5uI/g4uyZCKKAKGPMBtvz37ASwwkRqQJg+34yu42NMd8ZY0KMMSF+fq5dB0QpV7TuwCkGf7+B8iW8+OWxdtQsX+LqGxkD85+DY5utsQKVg+wfaBFgt0RgjDkOHBWR+rZFtwK7gLnAUNuyocAce8WglHJOK/fG8uAPG6laxodfHm1H1TI+edtw/VcQNhVueREa9bZvkEWIvccRPAVMEREv4CAwDCv5/CIiDwNHgH52jkEp5UT+3HWCJ6ZspXbFkvz8cGvKlyyWtw33L4UlL0PDnnDLSPsGWcTYNREYY0KBkGxeutWe76uUck7zt0fz7PRQGlctzY/DWlO6uGfeNozbD78+BBUbQR8tH3Gt9LellCoUZm6J4ulp22heoww/P3wNSSD5HEx7ANw94IGpUCwPN5TVP2iJCaWUw03ZcJjRs3fQoU55xg8JobhXHk9NmRnw28Nw5hAMmQtlc5mHQOVIE4FSyqG+X3OIN+bvokuDinw1sAXenu5533jpa7D/T+jxCQR0sFuMRZ0mAqWUw3y5fD8fLN7DHU0q89kDzfHyuIbW6rDp8PfnVumIkIfsF6QLyDURiEg7YBBwM1AFuADsABYAPxtjztk9QqVUkWOM4eM/9zLur/30Dvbno37N8HC/hiQQtQXmPg0BN8Pt79ovUBeR429eRP4AhgOLgduxEkEj4GXAG5gjIr0KIkilVNFhjOHthRGM+2s/94dU5+P7gq8tCcTHwPQBUKoS9JsM7nm8qaxylNsVwWBjTNwVyxKBrbavj0REx24rpfIsM9Pw6tyd/LT+MEPb1eTVno1xc7uGYnBpyTBjIKQkwOA/oUR5+wXrQnJMBNkkAUTkVqA4VmnptOzWUUqp7GRkGl6cuZ1ft0TxaMdAXryjAXItFUGNgXnPwLEtcP8UqNTYfsG6mDzfLBaRj4BUIBP4D3CnvYJSShUtaRmZ/PeXMOaFRfPMrXV5tmvda0sCAH+Pg+3TofNoaNjDPoG6qBwTgYh8CLyR5YZwDeA+2+NwewemlCoaUtIzeHraNhbvPMHI2xvwn061r30ne5fAn69Aoz7Q8YX8D9LF5XZFMBuYISILgK+AH4H1WDeKvyuA2JRSTi45LYPHft7Cij2xvNazEQ92qHXtO4ndCzMfhspNoM9XOsGMHeR2j2AtcLuIDAYWAZ8bY9oUWGRKKaeWlJLO8MmbWX/oFO/0DaJ/6xrXvpMLZ2B6f3D3ggemgVceSlGra5Zb91EPEbkLOAHcDTQXkbki0rTAolNKOaX45DSGTtzIhkOn+Pi+ZteXBDLSbeUjDsP9P0OZ6vkfqAJybxr6HQjF6iU00BgzVET8gddFxBhjHimQCJVSTuXs+VSGTNzIruh4vhjQgjuDqlzfjpa+CgeWQc/PoWa7/A1S/UNuiaCmMaaHbS6B9QDGmGhguIgEF0h0SimnEpeYwqAJGzgYm8S3g1tya8NK17ej0Kmw7gto/Si0HHr19dUNyS0RfCcioYABPsr6gm2eAaWUuuREfDIDxq/n2NkLfP9gCDfXvc4pZo9ussYL1OoI3d/K3yBVtnK7WTwOGFeAsSilnFTUmfMMnLCBuIQUJg9rTZvA6xzxe+6YNXLY11/LRxSg3G4WvywiZXN5vYuI6KgOpVxcZFwS93+7njNJqfw8vM31J4G0C1YNodQk6D8dipfL30BVjnJrGgoH5otIMlZtoVisMQR1gWBgKfC23SNUShVa+08mMGD8BtIyMpn6SFuaVC19fTsyBuY+BTFh0H8aVGyYv4GqXOXWNDQHq8JoXaADVvXReOBnYIQx5kLBhKiUKox2Rccz+PsNiAgzHm1HvUqlrn9naz+F8F+hyxiof0f+Bany5Kq1howx+4B9BRCLUspJhB09y5CJGynu5c6U4W0I9LuBeYL3LIKlY6FxX7j5//IvSJVnOkOZUuqabIo8zbAfNlG2hCdTh7elerni17+z2D0wczhUaQq9v9TyEQ6iiUAplWd/74/j4cmbqVLamymPtKFKaZ/r39n50zDtAfD0gQemgtcNJBR1QzQRKKXyZPnukzz68xZqlS/Bz8Pb4Feq2PXvLCMdfhsG56Jg6HwoXS3/AlXX7Krzw4lIPRFZJiI7bM+bisjL9g9NKVVYLNpxnBE/baZepZJMG9H2xpIAwJKX4eAKuOtjqKG1LB0t20QgIo+JSAPb0/HAS0AagDFmO/BAwYSnlHK0OaHHeGLqVppULc2U4W0pV8Lrxna49SfY8DW0fRxaDM6fINUNyemK4Geskz9AcWPMxiteT7dfSEqpwuKXTUd5dkYoITXL8tPDbSjtc4MjfY+sh/nPQWBnuO2N/AlS3bBsE4ExJhEYbnsaJyK1sWoOISL3AjEFE55SylF+XBfJ/2Zu56Y6FZg0rDUli93gLcVzUTBjkFVO+t6J4K63KAuL3AaUpdkePoE1I1kDETkGHAIGFkBsSikHGb/qIG8tjKBrw0p8ObA5xTzcb2yHqeet8hFpyfDgAi0fUcjkJSUbY0xXESkBuBljEkTkOuabU0oVdsYYxv21n4//3MtdQVX49IFgPN2v2qfkajuFOU9AzHYYMAP86udPsCrf5OUIzwQwxiQZYxJsy36zX0hKKUcwxvDB4j18/Ode+raoymf5kQQAVn8EO2dB11ehXvcb35/KdzleEdh6DTUGSotI3ywv+WIVn1NKFRHGGF6fv4sf1kYyoE0N3uzdBDe3fBjlu3sh/PUGBPWDDs/e+P6UXeTWNFQf6AGUAXpmWZ4A6DSVShURpxJTeHXuTuZvj2FYhwBe6dEIyY9SDycjYNYj4N8ceo3T8hGFWF6qj7YzxqwrwJiUUgXAGMOsrcd4c8EuElPSeaF7fR7vVDt/ksDF8hFeJazyEZ43UIpC2V1uTUP/M8a8DwwQkf5Xvm6MedqukSml7ObwqSRGz97Bmv1xtKxZlnf6Bt1YGemsMtLg16EQHw0PLrRmG1OFWm5NQxG275sLIhCllP2lZWQyYfUhPl26Fy93N97o04SBrWvkz/2AixaPhkOroM/XUL1V/u1X2U1uTUPzbN8nF1w4Sil7CT16lhdnbmf38QRub1yZ13o1pnLpfO73sWUSbPwW2j0JwQPyd9/KbnJrGpqHbTRxdowxvewSkVIqXyWmpPPRkj1M+juSSqW8+XZwS7o3rpz/b3T4b1jwPNS+FW57Pf/3r+wmt6ahDwssCqWUXSyLOMGY33cQE5/M4LY1eaF7fUp532C9oOycPQIzBkPZmlb5CLcbHImsClRuTUMr8+MNRMQd6z7DMWNMDxEpB8wAAoBI4D5jzJn8eC+llOVkfDJj5+1iQXgM9SqV5LcB7WlZs6x93iw1ySofkZEG/aeDTxn7vI+ym6uWmLBNXv8O0IgsA8mMMYF5fI9nsG48+9qevwgsM8a8KyIv2p6PvJaglVLZy8w0zNh8lLcXRpCSnsnz3eoxomNtvDzyYYRwdoyB3/8DJ3bCgF+gQl37vI+yq7zUGvoBeBX4BOgMDAPy1MVARKoBdwFvAf+1Le4NdLI9ngysQBOBUjds/8lERs0KZ2PkadoGluPtu4NubFL5vFj1AeyaY5WUrnubfd9L2U1eEoGPMWaZiIgx5jDwmoisxkoOV/Mp8D8gawflSsaYGABjTIyIVMxuQxEZAYwAqFGjRh7eSinXlJKewdcrDvDV8gP4eLnz/j1N6RdSLX8GhuUmYh4sfwuaPgDtn7Lveym7yksiSBYRN2CfiDwJHAOyPXlnJSI9gJPGmC0i0ulaAzPGfIdV/pqQkJAcey8p5co2RZ7mpVnh7D+ZSK9m/ozp0ejGp5HMixM7YdajULUl9PxMy0c4ubwkgmeB4sDTwBtYzUND8rBdB6CXiNyJdW/BV0R+Bk6ISBXb1UAV4OT1ha6U6zp3IY33Fu1m6oYjVC3jww/DWtG5/lU/n+WPpFNW+YhipeD+KeCpNSidXV7uIAUYYxKNMVHGmGHGmHuAq7bVGGNeMsZUM8YEYM1x/JcxZhAwFxhqW20oMOc6Y1fK5RhjWBgeQ9ePVzJ94xGG31SLJc91LLgkcLF8RMIJq4aQb5WCeV9lV3m5IngJ+DUPy/LqXeAXEXkYOAL0u879KOVSos9e4JU5O1kacYLG/r5MHNqKoGqlCzaIRS9C5Gq4+zuo1rJg31vZTW4ji+8A7gSqisjnWV7y5RonrzfGrMDqHYQx5hRw67UGqpSrysg0/Lz+MO8v2k2GMYy6swEPdaiFR35MGnMtNn0PmyZAh2eg2f0F+97KrnK7IojGGgjWC9iSZXkC8Jw9g1JKWXYfj+fFmeGEHj1Lx3p+vNWnCdXLFS/4QCLXwB//g7rd4Na8dBhUziS3kcVhIrID6KaF55QqWMlpGXy+bB/frTpIaR9PPnsgmF7N/O3fJTQ7Zw7DL0OgbC24Z4KWjyiCcr1HYIzJEJHyIuJljEktqKCUcmV/749j1OxwIk+d596W1Rh9Z0PKlvByTDApiVb5iMx0q3yEdwHfk1AFIi83iw8Da0VkLpB0caEx5mO7RaWUCzqTlMpbCyP4bUsUAeWLM3V4G9rXqeC4gDIzYfajcHIXDPwNKtRxXCzKrvKSCKLbGY23AAAcpElEQVRtX278c4SwUiofGGOYExrN6/N3EX8hjSc61+apLnXx9nRwE8zK92D3fOj+NtTR/h1F2VUTgTFmLICIlLKemkS7R6WUizh6+jyjf9/Bqr2xBFcvwzt9g2hYxffqG9rbrjmw8l0IHghtH3d0NMrO8lJ9tAnwE1DO9jwOGGKM2Wnn2JQqstIzMpm49hAf/7kXdxHG9mrMoLY1cc/PKSOv1/FwmP0YVGsNPT7R8hEuIC9NQ98B/zXGLAew1Q0aD7S3Y1xKFVnhUed4cdZ2dkbH07VhJV7v3Rj/Mj6ODsuSFAfTBoB3Gbj/Z/AogLpFyuHykghKXEwCYA0OE5ESdoxJqSIpKSWdT/7cy8S1hyhfshhfD2zB7U0qO6ZLaHbSU61uokknYdgfUKqSoyNSBSQvieCgiIzBah4CGAQcsl9IShU9y/ec5OXZOzh29gID2tRg5O0NKO1jhykjr5cx8McLcHgt3PM9VG3h6IhUAcpLIngIGAvMwpqQZhXW5DRKqauITUjh9fm7mBcWTZ2KJfn1sXa0Cijn6LD+KT0FVn0IWybBTf+FoHsdHZEqYHnpNXQGqwS1UiqPjDH8ujmKtxZGcCE1g+e61uOxToEU8yhEo3KNgYi58OcrcCYSgvpBlzGOjko5QG5F5+bmtqExplf+h6OU8zsYm8io2eGsP3ia1gHleLtvE+pULGRDcI5tgcWj4cg6qNgIBs3SsQIuLLcrgnbAUWAasIE8zlOslKtKTc/ku1UH+Pyv/RTzcOOdvkHcH1Idt8LQJfSic8dg2VjYPgNK+FmzizUfrPWDXFxuiaAycBvQHxgALACm6fgBpf5ty+EzjJoVzp4TCdzVtAqv9mhERd9CNHNXSiKs/Qz+HgcmE27+P+jwLHgXgsFryuFyqz6aASwCFolIMayEsEJEXjfGjCuoAJUqzBKS0/hg8R5+Wn+YKr7eTBgSQtdGhajbZWYGhE2DZW9A4nFoci90fRXKXHWSQeVCcr1ZbEsAd2ElgQDgc6zeQ0q5vMU7j/PqnJ2cSEjmwfYB/F+3+pQslpeOeAXk0CpYPMoaKVyttTVArHorR0elCqHcbhZPBpoAfwBjjTE7CiwqpQqx4+eSeXXuDhbvPEGDyqX4ZnBLgquXcXRYl8Xthz/HwJ6FULoG3DsRGvfVUhEqR7l9fBmMVXa6HvB0ltGPglV8ThsXlUvJzDRM2XiE9//YTWpGJiNvb8Dwm2vhWdBTRubk/GlY+T5sGg8ePtD1NWjzH/AsRPcqVKGU2z2CQvLXrZTj7T2RwEuzwtly+Aw31anAW3c3oWb5QlJpJT3Vmkt45XuQEg8thkLnUVCyoqMjU06iEDVoKlX4JKdl8OXy/Xyz8gAli3nw8X3NuLt51cJRH8gYq/lnyRg4fQBqd4Fub0GlRo6OTDkZTQRK5WD9wVOMmhXOwbgk+javyui7GlK+ZCGpxhkTZg0Ii1wNfg1g4Eyo29XRUSknpYlAqSucPZ/KOwt3M2PzUaqX8+HHh1rTsZ6fo8OyxMfAX29A6FQoXg7u+ghaPAju+q+srp/+9ShlY4xh/vYYxs7byZnzaTx6SyDP3loPH69CMOo2NQn+/gLWfmpNJN/haWtQmE4mr/KBJgKlgKgz5xnz+w6W74mlabXSTH6oNY39C8FJNjPTKgex7HVIiIZGfazeQOVqOToyVYRoIlAuLSPT8MPaQ3y0ZC8iMKZHIx5sH1A4poyMXGsNCIsJBf8W0O8HqNHW0VGpIkgTgXJZO6PP8dKscLZHnaNzfT/e6NOEamWLOzosOHUAlr4KEfPAtxr0nQBN7gE37dGt7EMTgXI5F1Iz+HTZXiasPkTZ4l58MaA5dwVVcXyX0AtnrAliNnwL7l7Q5WVo+wR4FYLkpIo0TQTKpazaG8vo38M5evoCD7Sqzkt3NKR0cQdPGZmRBpt/gBXvWMmgxWDoPBpKVXZsXMplaCJQLuFUYgpvLohg9rZjBFYowfQRbWkbWN6xQRkDexfDkpfh1D6o1RG6vw2Vgxwbl3I5mghUkWaMYdbWY7y5YBeJKek83aUOj3eug7eng7uEHt8BS0bDwRVQvi70nwH1umthOOUQmghUkRUZl8To38NZu/8ULWuW5Z2+QdSr5OApIxNOwPI3YetP4FMG7ngfQh4Cdwc3TymXpolAFTlpGZmMX32Qz5buw8vdjTf7NGFA6xqOnTIy7QKs+wLWfArpKdDuCej4PPiUdVxMStloIlBFSujRs7w4czu7jydwe+PKjO3dmEqOnDIyMxN2zISlr0F8FDTsCV3HQvnajotJqStoIlBFQmJKOh8u3sPkdZFUKuXNt4Nb0r2xg3vdHFlvDQg7tgWqNIO+30LATY6NSalsaCJQTm/prhOMmbOD4/HJDG5bkxe616eUtwPb3M9EWlcAO2dDqSrQ5xtoer8OCFOFliYC5bROxiczdt4uFoTHUK9SSb4Y0J6WNR3Y5p58DlZ/BOu/BjcP6PQStH8KvArJBDZK5UATgXI6mZmG6ZuO8s4fEaSkZ/J8t3qM6FgbLw8HfeLOSIetk2D529Z0kcEDrFHBvv6OiUepa2S3RCAi1YEfgcpAJvCdMeYzESkHzAACgEjgPmPMGXvFoYqW/ScTGTUrnI2Rp2kbWI637w4i0K+k4wLat9QaDxC7G2reBN3fAv9gx8Wj1HWw5xVBOvB/xpitIlIK2CIifwIPAsuMMe+KyIvAi8BIO8ahioCU9Ay+XnGAr5YfwMfLnffvbUq/ltUcVx/oxC5rRPCBZVAuEO6fAg3u0gFhyinZLREYY2KAGNvjBBGJAKoCvYFOttUmAyvQRKBysSnyNC/NCmf/yUR6NfPnlZ6NqOCoKSMTY2HF27BlEhQrBd3fgVbDwcPLMfEolQ8K5B6BiAQAzYENQCVbksAYEyMiFQsiBuV8zl1I471Fu5m64QhVy/jww7BWdK7voD+XtGTY8DWs+gjSL0DrEXDLSGu6SKWcnN0TgYiUBGYCzxpj4vN6KS8iI4ARADVq1LBfgKrQMcbwx47jvDp3J6cSU3jk5lo8d1s9ins5oG+DMVY30KWvwtkjUP9OuO11qFC34GNRyk7s+p8lIp5YSWCKMWaWbfEJEaliuxqoApzMbltjzHfAdwAhISHGnnGqwiP67AVembOTpREnaOzvy8ShrQiq5qApI6M2w6KXIGojVAqCIXMgsJNjYlHKjuzZa0iA74EIY8zHWV6aCwwF3rV9n2OvGJTzyMg0/LQukg8W7yHTwOg7GzKsQwAe7g7oEnr2CCwdCzt+g5KVoNcXVpdQt0Iwib1SdmDPK4IOwGAgXERCbctGYSWAX0TkYeAI0M+OMSgnEBETz4uzwgk7epaO9fx4q08TqpdzwKxcKQmw5hNY96X1vOP/oMMzUMyB3VOVKgD27DW0BsjphsCt9npf5TyS0zL4fNk+vlt1kNI+nnz2QDC9mvkXfJfQzAzY9hP89SYkxVrlIG59BUpXK9g4lHIQHVmsHGLt/jhGzQ7n8Knz9GtZjVF3NqRsCQd0wTywHBaPhpM7oUY7GDADqrYs+DiUciBNBKrAGGMIizrH5L8jmb3tGAHlizN1eBva16lQ8MHE7oElY2DfYihTE/pNhka9dUCYckmaCJTd7T+ZyNzQY8wJi+bwqfN4ebjxeKfaPH1r3YKfMjLplDVJ/OaJVjG4296ANo+Ch4MGqClVCGgiUHYRc+4C88KimRMazc7oeNwE2teuwBOd69C9cWVK+xRwmej0FNj4Haz8AFITIWSYVR20hAOuRpQqZDQRqHxz9nwqf+w4zpzQY2w4dBpjoFm10ozp0YieTatQ0REzhRkDEXPhz1eseQLqdrOuAio2KPhYlCqkNBGoG3IhNYOlESeYExrNyr0nScswBFYowbO31qNXsD+1KjiwFv+xrdaN4CN/Q8VGMGgW1NEOa0pdSROBumZpGZms3R/HnNBoluw8TlJqBpV8i/Fg+wB6B1elsb+v46qCApw7Bsteh+3ToYQf9PgUmg8Gd/1zVyo7+p+h8sQYw9YjZ5gTGs2C7TGcSkrF19uDns386RXsT5ta5XF3c3CPm5REWPsZ/D0OTCbc9F+46Tnw9nVsXEoVcpoIVK72HE9gTugx5oZFE3XmAsU83OjaqBK9m/lzS30/inkUgrILmRkQNg2WvQGJx6HJPXDrq1C2pqMjU8opaCJQ/xJ15jzzwmKYE3qM3ccTcHcTbqpTgf/eVo/bGlVy7MTwVzq0ChaPguPhUK0V3P8TVG/t6KiUciqaCBQAp5NSWRAew9zQY2yKtGYObVGjDGN7NebOoCr4lSpk/ezj9sOfY2DPQihdA+6dCI376oAwpa6DJgIXlpSSztKIE/y+7Rir98WRnmmoW7EkL3SvT8+m/tQo74DCb1dz/jSsfB82jQcPH6sJqO3j4OmArqlKFRGaCFxManomq/fFMic0mj93neBCWgb+pb15+OZa9AmuSoPKpRzb4ycn6amwaQKsfA9S4qHFUOg8CkrqBHdK3ShNBC4gM9Ow+fAZfg89xsLwGM6eT6NMcU/6tqhK7+CqhNQsi5uje/zkxBir+WfJGDh9AAI7Q/e3oFJjR0emVJGhiaCIMsYQEZPAnLBjzAuNJvpcMj6e7nRrXInewf7cVMcPLw8HTPpyLWLCrAFhkauhQn0Y+BvU6ar3AZTKZ5oIipgjp84zN+wYc0Kj2XcyEQ83oWM9P0be0YCuDStRopgTHPL4GPjrDQidak0Of+eH0HKYDghTyk70P6sIiEtMYcF2q7vn1iNnAWgdUI43+zThzqAqlHNEnf/rkZoEf38Baz+FzHRo/xTc/H/gU8bRkSlVpGkicFIJyWks2XmCOWHRrN0fR0amoUHlUoy8vQG9gv2pWsbH0SHmXWYmbJ9hlYVIiLbmBeg6FsrVcnRkSrkETQROJCU9g5V7rB4/SyNOkJKeSbWyPjx2SyC9mlWlfuVSjg7x2kWutQaExYSCfwtrPEDNdo6OSimXoomgkMvINGw4dIq5odEsDI8hPjmd8iW8eKBVdXoFV6VFjTKFs7vn1Zw6AEtfhYh54FsV+o6HJveCWyG/ga1UEaSJoBAyxrDjWDxzQo8xb3s0J+JTKOHlTvfGlekV7E+HOhXwdHeyE2Z6qtX9M3Y3HP4bNv8A7l7Q+WVo9wR4FcLBa0q5CE0EhcihuCTmhkYzJ+wYB2OT8HQXOtWvSO9gf25tUAkfr0JQ4O1q0pLh1D5rTuDY3bavPdYVgMmw1hF3CB4AXV6GUpUdG69SShOBo52MT2bedqvGT1jUOUSgTa1yPHJzIHc0qUyZ4oW0x0/qeYjbm+WEb/t+5pBVAhqsE365QPCrDw17gV8D63GFuuDpRDezlSriNBE4QHxyGotsUzquO3CKTANNqvoy+s6G9GhWhSqlC9FJMiUBYvf+89N97G44ewQw1jpunlC+DlQOgqB+1snerwGUr62TwivlBDQRFJDktAyW7z7JnNBo/tpzktT0TGqWL86TnevQK7gqdSqWdGyAF87aPuHvhpNZTvrxUZfXcfeCCvWgWgg0H3T5hF8uENwLUWlqpdQ10URgRxmZhr8PWFM6Lt5xnISUdCqULMbANjXoHVyVZtVKF3yPn/On//3pPnYPJMRcXsfDB/zqQUCHyyd7vwZQpqaO7lWqCNL/6nxmjCEs6pzV4ycshrjEFEoV8+D2JpXpHVyVtoHl8LB3jx9jICn23yf72N3W8os8S1gn+sDOWU749a0TvnbjVMplaCLIJ/tPJjI39BhzwqI5fOo8Xu5udGlg9fjp3KAi3p526PFjDCQcz3Kij7h8wr9w5vJ6xXytk3y92y9/uverb/Xf1xO+Ui5PE8ENiDl3gflhMfweeoyd0fG4CbSvXYEnOtehe+PKlPbJp3ZzY+Bc1L+7ZMbugZRzl9fzLgMVG1olGvwaXv6UX6qyVuxUSuVIE8E1Ons+lT9sPX42HDqNMdCsWmnG9GhEz6ZVqOh7AzNlZWbC2cP/bs6J2wupiZfXK+FnneCb9rv86d6vgbVcT/hKqWukiSAPLqRmsGz3CX7fFs3KvSdJyzAEVijBs7fWo1ewP7UqlLi2HWZmwJnIbG7a7oX0C5fXK1XFOsln7aFToT6UKJ+vP59SyrVpIshBekYma/bHMTc0msU7j5OUmkEl32I82D6A3sFVaezve/UePxlpcPrgv2/axu2DjJTL6/lWs070ITf986atll9WShUATQRZGGPYeuQMc0KjWbA9hlNJqfh6e9CzmT+9gv1pU6s87tlN6ZieYpVQuPQJ/2JZhf1WXf2LytS0TvK1u1y+aVuhLnj7FtwPqZRSV9BEAOw9kcCcUGtWr6gzFyjm4UbXRpXo3cyfW+r7UczD1uMn7QKcyKaOzumDWerouEHZAOtmbf07/1lWwesam5CUUqoAuGwiiDpznnlh1qxeu48n4O4mdKhTgee61qNb3RKUSjgEscthedY6OpFcKqsg7lYJhYoNoHGfyyf88nW0jo5Syqm4VCI4nZTKwnDr5L8p8gwlOc9dVeIZ0zKB5t7HKX5uP6zaA/OOXN7IzdP6NO8fDM0eyFJWoTZ4FNKCcEopdQ2KfCJISklnZdheQrduIClqB4EcY2Sx49QvHU2plBNwBuvLvZhVR6d6a2gxxDrhV2xoNfNoHR2lVBFWpBPB+on/o/bhGdwpZ7kTwAMyPXwQv/qI3xVlFcoGgJsT1PtXSql8VqQTgUfZqhxJaM+FwKZUq9cCt4oNcCtdXcsqKKVUFg5JBCJyO/AZ4A5MMMa8a4/3Cbn7GeAZe+xaKaWKjAL/aCwi7sCXwB1AI6C/iDQq6DiUUkpZHNFG0hrYb4w5aIxJBaYDvR0Qh1JKKRyTCKoCR7M8j7ItU0op5QCOSATZFegx/1pJZISIbBaRzbGxsdlsopRSKj84IhFEAdWzPK8GRF+5kjHmO2NMiDEmxM/Pr8CCU0opV+OIRLAJqCsitUTEC3gAmOuAOJRSSuGA7qPGmHQReRJYjNV9dKIxZmdBx6GUUsrikHEExpiFwEJHvLdSSql/EmP+dZ+20BGRWODwdW5eAYjLx3BU/tDjUvjoMSmcbuS41DTGXPUmq1MkghshIpuNMSGOjkP9kx6XwkePSeFUEMdFi+4opZSL00SglFIuzhUSwXeODkBlS49L4aPHpHCy+3Ep8vcIlFJK5c4VrgiUUkrlwqkSgYhMFJGTIrIjy7JgEVkvIqG22kStc9j2DRHZbltviYj4X/F6DRFJFJHn7f1zFCUi4i0iG0UkTER2ishY2/LXROSY7fcdKiJ35rB9ruvpcbk+IhIpIuEX/y9sy/J0TGzrPiUie2zH9P0rXtNjkk9EpIyI/CYiu0UkQkTaXctxyrc4nKlpSEQ6AonAj8aYJrZlS4BPjDF/2H5h/zPGdMpmW19jTLzt8dNAI2PMY1lenwlkAhuMMR/a/6cpGkREgBLGmEQR8QTWYM0GdDuQeLXfpYi8ltt6elyuj4hEAiHGmLgsy14jb8ekMzAauMsYkyIiFY0xJ7O8rsckn4jIZGC1MWaCreROceBZrnKcbMcy0hgzKT/icKqpKo0xq0Qk4MrFgK/tcWmyKWBn2zY+y9MSZKl4KiJ9gINAUn7F6iqM9Uki0fbU0/aVL58u9Lg4zH+Ad40xKQBXJAE9JvlERHyBjsCDALb5WVKtz1YFy6mahnLwLPCBiBwFPgReymlFEXnLtt5A4BXbshLASGBsAcRaJImIu4iEAieBP40xG2wvPWlrjpsoImVz2cW/1tPjcsMMsEREtojIiCzL83JM6gE3i8gGEVkpIq1Aj4kdBAKxwA8isk1EJth+x5D3/518URQSwX+A54wx1YHngO9zWtEYM9q23hTgSdvisVhNS4k5badyZ4zJMMYEY5UUby0iTYCvgdpAMBADfJTD5jmtp8flxnQwxrTAmhL2CVuzal6PiQdQFmgLvAD8YmsC1GOSvzyAFsDXxpjmWFdZL5LDcRKRoIv3DYDHgNez3Ecof0ORGGOc6gsIAHZkeX6Oy/c6BIi3Pf4BCAUWZrOPmhf3AawGIm1fZ4HTwJOO/jmd9Qt4FXg+p2N2leMSoMfFLsfktWs5JsAioFOWdQ8AfnpM8v24VMZq57/4/GZgQU7HKZtj+mB+xeJU9whyEA3cAqwAugD7AIwxw7KuJCJ1jTH7bE97Abtt692cZZ3XsG7SfGH3qIsIEfED0owxZ0XEB+gKvCciVYwxMbbV7gZ2QLbHJaf19LhcJ1vzgpsxJsH2uBvWp8c8HRPgd6z/pRUiUg/wAuL0mOQvY8xxETkqIvWNMXuAW4FdOR0ne3KqRCAi04BOQAURicL69PkI8JmIeADJwIgcNn9XROpj9XY4jHVppW5cFWCyiLhjNTX+YoyZLyI/iUgwVlt1JPBoDtu/n8f1VN5VAmbbbjp6AFONMYuu4ZhMBCaK1U07FRhqbB9DVb57Cphi6zF0EBgGfF7Q/xNO1X1UKaVU/isKN4uVUkrdAE0ESinl4jQRKKWUi9NEoJRSLk4TgVJKuThNBMqpiEhlEZkuIgdEZJeILBSReiISIFmq0hYWIjJJRO61PV6RTa0spRzOqcYRKNdmK3MwG5hsjHnAtiwYq9/8UUfGppQz0ysC5Uw6Y41i/ubiAmNMqDFmddaVbFcHq0Vkq+2rvW15FRFZZavNskNEbrYVzJtkex4uIs/Z1q0tIotsRdtWi0gD2/J+tnXDRGTVlQGK5Qvb1coCoGKWl08DGbb1vhZr/oxLczjYlt8pVm36NSLyuYjMty0vYStAtslWoKy3bXljseaDCLUVKaubP79q5VIcXW9Dv/Qrr1/A01hFz7J7LYDLtXOKA962x3WBzbbH/weMtj12B0oBLbEqpl7cTxnb92VAXdvjNsBftsfhQNWs614RR1/gT9v+/bFq8tybzXrlssSxAmgKeGNd2dSyvTYNmG97/DYw6OL7AnuxyqmPAwbalnsBPo4+TvrlfF/aNKSKIk/gC1uzUQZWWWWATVilEzyB340xoSJyEAgUkXHAAqzSzSWB9sCvcrk2fDHb97XAJBH5BZiVzXt3BKYZYzKAaBH5K4cY77OVh/bAKtPRCOsK/aAx5pBtnWlcLpnSDegll2cF8wZqAOuA0SJSDZhlLtfTUirPtGlIOZOdWJ/gr+Y54ATQDAjB+qSMMWYV1on6GPCTiAwxxpyxrbcCeAKYgPV/cdYYE5zlq6FtH48BLwPVgZzK/+Zat0VEagHPA7caY5piJSBvrOq5OW4G3JMlnhrGmAhjzFSsIooXgMUi0uVqvxylrqSJQDmTv4BiIvLIxQUi0kpEbrlivdJAjDEmExiM1fyCiNQEThpjxmPNW9FCRCpgVeqcCYwBWhhrNrtDItLPtp2ISDPb49rGmA3GmFeAOKyEkNUq4AHbvYcqWPc1ruSLVXv+nIhUwpozAKyKuIFZehbdn2WbxcBTthvmiEhz2/dArKuIz4G5WE1MSl0TbRpSTsMYY0TkbuBTEXkRq9psJNYsdVl9Bcy0nciXc3laxU7ACyKShjW95hCgKtYMURc/FF2c4W4g8LWIvIzV1DQdCMOaDa8u1if0ZbZlWc3GKuEcjtWOvzKbnyNMRLZhXeEcxGpuwhhzQUQeBxaJSBywMctmbwCfAtttySAS6IGVLAbZfqbjwOs5/f6UyolWH1WqEBGRksaYRNvJ/ktgnzHmE0fHpYo2bRpSqnB5RKypCHdiNXF96+B4lAvQKwKllHJxekWglFIuThOBUkq5OE0ESinl4jQRKKWUi9NEoJRSLk4TgVJKubj/B9/WPN786fxPAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "smoker_mortality = [smoker_18_34_mortality_rate,smoker_35_54_mortality_rate,\n", + " smoker_55_64_mortality_rate,smoker_65_mortality_rate]\n", + "no_smoker_mortality = [no_smoker_18_34_mortality_rate,no_smoker_35_54_mortality_rate,\n", + " no_smoker_55_64_mortality_rate,no_smoker_65_mortality_rate]\n", + "ages = [\"18-34\",\"35-54\",\"55-64\",\"65+\"]\n", + "\n", + "fig = plt.figure()\n", + "plt.plot(ages,smoker_mortality,label = \"smokers\")\n", + "plt.plot(ages,no_smoker_mortality,label = \"non smokers\")\n", + "plt.xlabel(\"Classes d'ages\")\n", + "plt.ylabel(\"Mortalité (%)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On voit que pour les deux classes d'ages 18-34 et 65+, le taux de mortalité est le même pour les fumeurs et les non-fumeurs. En revanche, pour les classes d'age 35-54 et 55-64, le taux de mortalité des fumeurs est nettement plus élevé que celui des non-fumeurs. Cela peut s'expliquer par le fait que l'age est un critère qui influe sur le taux de mortalité." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Troisieme analyse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Afin de quantifier l impact de l age sur la mortalite, nous allons realiser une regression logistique. Nous pourrons ainsi analyser les donnees de mortalité lié au tabagisme sans être induit en erreur par la classification par age." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data = data.set_index(\"Age\").sort_index()\n", + "\n", + "Bool_Death = []\n", + "Ages = sorted_data.index\n", + "Status = sorted_data.loc[ : , \"Status\"]\n", + "\n", + "for it in Status:\n", + " if(it==\"Alive\"):\n", + " Bool_Death.append(1)\n", + " else:\n", + " Bool_Death.append(0)\n", + " \n", + "fig = plt.figure()\n", + "plt.plot(Ages,Bool_Death,label = \"Death\")\n", + "plt.xlabel(\"Ages\")\n", + "plt.ylabel(\"Mortalité\")\n", + "plt.legend()\n", + "plt.show()" ] }, { -- 2.18.1