{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse du risque de défaillance des joints toriques de la navette Challenger" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le 27 Janvier 1986, veille du décollage de la navette *Challenger*, eu\n", "lieu une télé-conférence de trois heures entre les ingénieurs de la\n", "Morton Thiokol (constructeur d'un des moteurs) et de la NASA. La\n", "discussion portait principalement sur les conséquences de la\n", "température prévue au moment du décollage de 31°F (juste en dessous de\n", "0°C) sur le succès du vol et en particulier sur la performance des\n", "joints toriques utilisés dans les moteurs. En effet, aucun test\n", "n'avait été effectué à cette température.\n", "\n", "L'étude qui suit reprend donc une partie des analyses effectuées cette\n", "nuit là et dont l'objectif était d'évaluer l'influence potentielle de\n", "la température et de la pression à laquelle sont soumis les joints\n", "toriques sur leur probabilité de dysfonctionnement. Pour cela, nous\n", "disposons des résultats des expériences réalisées par les ingénieurs\n", "de la NASA durant les 6 années précédant le lancement de la navette\n", "Challenger.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chargement des données\n", "Nous commençons donc par charger ces données:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateCountTemperaturePressureMalfunction
04/12/81666500
111/12/81670501
23/22/82669500
311/11/82668500
44/04/83667500
56/18/82672500
68/30/836731000
711/28/836701000
82/03/846572001
94/06/846632001
108/30/846702001
1110/05/846782000
1211/08/846672000
131/24/856532002
144/12/856672000
154/29/856752000
166/17/856702000
177/29/856812000
188/27/856762000
1910/03/856792000
2010/30/856752002
2111/26/856762000
221/12/866582001
\n", "
" ], "text/plain": [ " Date Count Temperature Pressure Malfunction\n", "0 4/12/81 6 66 50 0\n", "1 11/12/81 6 70 50 1\n", "2 3/22/82 6 69 50 0\n", "3 11/11/82 6 68 50 0\n", "4 4/04/83 6 67 50 0\n", "5 6/18/82 6 72 50 0\n", "6 8/30/83 6 73 100 0\n", "7 11/28/83 6 70 100 0\n", "8 2/03/84 6 57 200 1\n", "9 4/06/84 6 63 200 1\n", "10 8/30/84 6 70 200 1\n", "11 10/05/84 6 78 200 0\n", "12 11/08/84 6 67 200 0\n", "13 1/24/85 6 53 200 2\n", "14 4/12/85 6 67 200 0\n", "15 4/29/85 6 75 200 0\n", "16 6/17/85 6 70 200 0\n", "17 7/29/85 6 81 200 0\n", "18 8/27/85 6 76 200 0\n", "19 10/03/85 6 79 200 0\n", "20 10/30/85 6 75 200 2\n", "21 11/26/85 6 76 200 0\n", "22 1/12/86 6 58 200 1" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "data = pd.read_csv(\"shuttle.csv\")\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le jeu de données nous indique la date de l'essai, le nombre de joints\n", "toriques mesurés (il y en a 6 sur le lançeur principal), la\n", "température (en Farenheit) et la pression (en psi), et enfin le\n", "nombre de dysfonctionnements relevés. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspection graphique des données\n", "Les vols où aucun incident n'est relevé n'apportant aucun information\n", "sur l'influence de la température ou de la pression sur les\n", "dysfonctionnements, nous nous concentrons sur les expériences où au\n", "moins un joint a été défectueux.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il est important de garder l'sensemble du jeux de donner si l'on souhaite obtenir une regression logistique adaptée à la situation. Un premier biais semble être lié à ce subsetting." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#data = data[data.Malfunction>0]\n", "#data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Très bien, nous avons une variabilité de température importante mais\n", "la pression est quasiment toujours égale à 200, ce qui devrait\n", "simplifier l'analyse.*\n", "\n", "*Comment la fréquence d'échecs varie-t-elle avec la température ?*\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n", "import matplotlib.pyplot as plt\n", "\n", "data[\"Frequency\"]=data.Malfunction/data.Count\n", "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "À première vue, ce n'est pas flagrant mais bon, essayons quand même\n", "d'estimer l'impact de la température $t$ sur la probabilité de\n", "dysfonctionnements d'un joint. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimation de l'influence de la température\n", "\n", "Supposons que chacun des 6 joints toriques est endommagé avec la même\n", "probabilité et indépendamment des autres et que cette probabilité ne\n", "dépend que de la température. Si on note $p(t)$ cette probabilité, le\n", "nombre de joints $D$ dysfonctionnant lorsque l'on effectue le vol à\n", "température $t$ suit une loi binomiale de paramètre $n=6$ et\n", "$p=p(t)$. Pour relier $p(t)$ à $t$, on va donc effectuer une\n", "régression logistique." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -3.9210
Date: Mon, 24 May 2021 Deviance: 3.0144
Time: 13:26:19 Pearson chi2: 5.00
No. Iterations: 6 Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740
Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110
" ], "text/plain": [ "\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: Mon, 24 May 2021 Deviance: 3.0144\n", "Time: 13:26:19 Pearson chi2: 5.00\n", "No. Iterations: 6 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": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import statsmodels.api as sm\n", "\n", "data[\"Success\"]=data.Count-data.Malfunction\n", "data[\"Intercept\"]=1\n", "\n", "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n", "\n", "logmodel.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'estimateur le plus probable du paramètre de température est 0.0014\n", "et l'erreur standard de cet estimateur est de 0.122, autrement dit on\n", "ne peut pas distinguer d'impact particulier et il faut prendre nos\n", "estimations avec des pincettes.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'estimateur le plus probable du paramètre de temperature este de -0,1156 avec une erreur standard de 0,115. \n", "ce resultat est assez conforme avec l'etude de *Dalal et al.*. Neanmoins la deviance associe au 21 degre de liberte est tres differente et je ne retourve pas la deviance de 18 mentionnee dans l'etude. Il semble que cet ecart provienne du fait que je ne donne par le nombre de replication de l'experience pour chaque etat.\n", "voyons cela ...\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -23.526
Date: Mon, 24 May 2021 Deviance: 18.086
Time: 13:26:24 Pearson chi2: 30.0
No. Iterations: 6 Covariance Type: nonrobust
\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068
Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023
" ], "text/plain": [ "\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: -23.526\n", "Date: Mon, 24 May 2021 Deviance: 18.086\n", "Time: 13:26:24 Pearson chi2: 30.0\n", "No. Iterations: 6 Covariance Type: nonrobust\n", "===============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "-------------------------------------------------------------------------------\n", "Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068\n", "Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023\n", "===============================================================================\n", "\"\"\"" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import statsmodels.api as sm\n", "\n", "data[\"Success\"]=data.Count-data.Malfunction\n", "data[\"Intercept\"]=1\n", "\n", "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit),var_weights=data['Count']).fit()\n", "\n", "logmodel.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimation de la probabilité de dysfonctionnant des joints toriques\n", "La température prévue le jour du décollage est de 31°F. Essayons\n", "d'estimer la probabilité de dysfonctionnement des joints toriques à\n", "cette température à partir du modèle que nous venons de construire:\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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NHDlygXNuUE3tAjlbprqkOfQTIQbIAk4DEoHPzewL59yqgxZy7gngCYBBgwa5ESNGBLD5n8rOzqa2y4ab7OxsbvrFCJq0Wc9dby8jp6Q1t47uEeqyaiXa3pdo6Eu09APUl6MVSLjnAu2qTGcCm6pps905txfYa2YfA/2AVUhAfnlCB1ZvLWRq9lq6NE/hgiyNIikitRfIMfccoKuZdTSzOOBi4K1D2rwJnGRmMWaWBAwFlge31OhmZtx1Ti9O6NyU219fwoLvdoa6JBGJYDWGu3OuHLgBmIMvsF92zi01s0lmNsnfZjnwLrAY+Ap4yjn3bf2VHZ1ivR4evWwgrdMTuO75BeTt1hk0IlI7AZ3n7pyb7Zzr5pzr7Jy7xz/vMefcY1XaTHHO9XTO9XbOPVhfBUe79KQ4pv1yECVllUx4VmfQiEjt6ArVMNSlRSoPXzqAlVvy+a+XdaNtETl6CvcwNbJ7C/77rON4d+kWHv7P6lCXIyIRRgOHhbFrTuzIii0FPPj+arq3TOXMPq1DXZKIRAjtuYcxM+Oe83ozsH06v3v5G5Zt0m36RCQwCvcwt/8+rGmJsVz73Hx2FJaEuiQRiQAK9wjQIjWBJ67IYnthCb/+59e6i5OI1EjhHiH6ZqZz/wV9+XL9Tv749tJQlyMiYU5fqEaQMQPasnxzPo9/vI6erdO4dGj7UJckImFKe+4R5veje3BKt+bc+da3zN+gIQpEpHoK9wjj9RgPXzyAzGOSmPTCAjZpiAIRqYbCPQKlJcXy5BVZFJdVct3zCyguqwh1SSISZhTuEapLi1T+dlF/luTt4fbXl1DTTVdEpHFRuEew03u25Hend+ONhXlM+3R9qMsRkTCicI9wN4zswuherfjz7OV8snpbqMsRkTChcI9wHo/xwLh+dGmRwg0vLmTjjn2hLklEwoDCPQqkxMfw5BW+++Ve+5zGgBcRhXvUOLZpMv+4dACrtxZwyyvf6AtWkUZO4R5FTuranNvO7MG/v93CIx+uCXU5IhJCCvcoc+1JnRjTvw1/eW8V7y/7IdTliEiIKNyjjJlx3wV96dWmCTfPXMSarYWhLklEQkDhHoUSYr08Pn4Q8TEeJj43nz1FZaEuSUQamMI9SrVNT2Tq5Vls3LmP38xYSIVusi3SqCjco9iQjhnceU4vsldu44G5K0Ndjog0II3nHuUuH9qeZZvymZq9lp6tm3B2vzahLklEGoD23KOcmfHHc3ox6NhjmPzqN3ybtyfUJYlIA1C4NwJxMR6mXp7FMUlxTHxuPtt1k22RqKdwbySap8bz5BWD2LG3lF+9sIDSct1kWySaKdwbkd5t0/i/sX3J2bCLO99aqiEKRKKYvlBtZM7t35blmwt47KO19GydyvhhHUJdkojUA+25N0KTR3Xn1B4t+OPby/hs7fZQlyMi9UDh3gh5PcZDF/enQ7Nkrv/n1xoDXiQKKdwbqdSEWJ66YhCVDiY8l0NBsYYoEIkmCvdGrEOzZB69bCBrt+3l5hmLNESBSBRRuDdyw7s0486ze/LBiq1MmaMhCkSihc6WEcYffywrt/jOoOnWMoXzB2aGuiQRqaOA9tzNbLSZrTSzNWZ22xHaDTazCjMbG7wSpb6ZGXed04thnZpy22tLWPDdzlCXJCJ1VGO4m5kXeAQ4E+gJXGJmPQ/T7n5gTrCLlPoX6/Uw9fKBtElPYOJzC8jdpTNoRCJZIHvuQ4A1zrl1zrlSYAZwbjXtbgReA7YGsT5pQOlJcUy7cjClFZVMeHa+zqARiWBW0yXo/kMso51zE/zT44GhzrkbqrRpC7wInApMA95xzr1azbomAhMBWrZsmTVjxoxaFV1YWEhKSkqtlg034diXpdsr+MuCYno383LzwHg8ZgEtF459qa1o6Uu09APUl/1Gjhy5wDk3qKZ2gXyhWt3/7EM/ER4EbnXOVdgRgsA59wTwBMCgQYPciBEjAtj8T2VnZ1PbZcNNOPZlBJCW+R1/mPUtnxS24M6zewW0XDj2pbaipS/R0g9QX45WIOGeC7SrMp0JbDqkzSBghj/YmwFnmVm5c25WUKqUBnf58ceybttenp63nk7NkjUGjUiECSTcc4CuZtYRyAMuBi6t2sA513H/czObju+wjII9wv2/nx/Hdzv2cudbS8nMSGJk9xahLklEAlTjF6rOuXLgBnxnwSwHXnbOLTWzSWY2qb4LlNDxeoyHLxnAca2bcMM/v2bZpvxQlyQiAQroPHfn3GznXDfnXGfn3D3+eY855x6rpu2V1X2ZKpEpOT6Gab8cTGpCLNc8m8OWPcWhLklEAqDhB6RGrdISmHblIPKLyrh6eg6FJeWhLklEaqBwl4D0apPGI5cNZOUPBdzw4teUV+g2fSLhTOEuARvRvQV/Orc32Su3cceb3+o2fSJhTAOHyVG5dGh7cnft49HstbRNT+SGU7uGuiQRqYbCXY7a5FHd2bKnmAfmrqJVWiJjszSKpEi4UbjLUTMz7rugL1sLSrjttcU0T43nlG7Na7WuWQvzmDJnJZt2F9EmPZHJo7ozZkDbIFcs9UXvX/jSMXeplbgY3yiS3Vqm8qsXFvDN97uPeh2zFuZx++tLyNtdhAPydhdx++tLmLUwL/gFS9Dp/QtvCneptdSEWKZfPZimKXFcPT2HLXuP7gyaKXNWUlRWcdC8orIK3REqQuj9C28Kd6mTFqkJPHvVEBzwwPxifsgP/CKnTbuLjmq+hBe9f+FN4S511ql5CtOvGkxhqeOKaV+xZ19g48C3SU88qvkSXvT+hTeFuwRF38x0bhqYwPrte7n62Rz2ldZ8FevkUd1JjPUeNC8x1svkUd3rq0wJIr1/4U3hLkHTs6mXBy/uz8KNu5j0wteUlh/5GPyYAW259/w+tE1PxIC26Ynce34fnW0RIfT+hTedCilBdVaf1tx3fl9+/9pifjtzEQ9fMgCv5/A3cBkzoK3CIILp/QtfCncJunGD25FfXMbd/1pOcryX+87vi+cIAS8iwadwl3ox4aROFBSX89AHq0mM9XLXOb040i0YRSS4FO5Sb27+WVf2lZbz5CfrSYjzctvoHgp4kQaicJd6Y2b891nHsa+0gsc/Wkd8jJffnd4t1GWJNAoKd6lXZsafzu1NaXklD3+wmliPceNpGklSpL4p3KXeeTy+gcYqKh1/eW8VMV4PvxrROdRliUQ1hbs0CK/HmHJhP8orHfe/uwKH49cjuoS6LJGopXCXBuP1GH8d1w8z+L93V+IcXD9SAS9SHxTu0qBivB7+Oq4/hm9UwfIKx02nddFZNCJBpnCXBuf1GH8Z1x+vx8Pf3l9FSXkFk0d1V8CLBJHCXULC6zGmjO1LXIyHR7PXUlxWyR2/OE4BLxIkCncJGY/H+PN5vYmP8fD0vPXsKy3nnvP6HHEsGhEJjMJdQsrMuPPsnqQmxPD3/6yhsKScv47rT1yMBiwVqQuFu4ScmfFfZ3QnJT6Ge/+9goLicqZePpCkOP3zFKkt7R5J2LjulM7cd34fPlm9jUuf/JJde0tDXZJIxFK4S1i5eEh7Hr0si2Wb87nw8c/J0/04RWpF4S5hZ3TvVjx71RB+yC/mvEfmsWxTfqhLEok4CncJS8M6N+WVScPwmDHu8c/5ZPW2UJckElEU7hK2erRqwhvXn0DmMYlc9UwOM3M2hrokkYihcJew1jotkVcmDWNY56bc+toS7n93BZWVLtRliYQ9hbuEvdSEWJ6+cjCXDGnP1Oy1/OqfC9hXWh7qskTCWkDhbmajzWylma0xs9uqef0yM1vs//nMzPoFv1RpzGK9Hv58Xm/u+EVP3lv2A2Onfs4mnUkjclg1hruZeYFHgDOBnsAlZtbzkGbrgVOcc32BPwFPBLtQETPjmhM7Mu3KwXy/cx9n//1Tvlq/M9RliYSlQPbchwBrnHPrnHOlwAzg3KoNnHOfOed2+Se/ADKDW6bIj0Z2b8Eb1w8nLTGWS5/8guc/34BzOg4vUpXV9J/CzMYCo51zE/zT44GhzrkbDtP+FqDH/vaHvDYRmAjQsmXLrBkzZtSq6MLCQlJSUmq1bLhRX2pvX5nj8cUlfLOtguFtYriiVxzx3uAMOhYt70u09APUl/1Gjhy5wDk3qMaGzrkj/gAXAk9VmR4P/P0wbUcCy4GmNa03KyvL1daHH35Y62XDjfpSNxUVle5v7610HW57x43620du/bbCoKw3Wt6XaOmHc+rLfsB8V0O+OucCOiyTC7SrMp0JbDq0kZn1BZ4CznXO7QhgvSJ15vEYN/+sG89cOZjNe4o5+++f8q/Fm0NdlkjIBRLuOUBXM+toZnHAxcBbVRuYWXvgdWC8c25V8MsUObIR3Vvwr5tOpHOLFK5/8WvumPUtxWUVoS5LJGRqDHfnXDlwAzAH3yGXl51zS81skplN8jf7H6Ap8KiZLTKz+fVWschhZB6TxMvXDePakzry/BffMeaReaz+oSDUZYmEREADZjvnZgOzD5n3WJXnE4CffIEq0tBmL9nM7CVbAFj1QwFnPfwJY/q3Zd6a7WzeU0yb9EQmj+rOmAFtg77tWQvzmDJnJZt2F9XrdgLxh1lLeOnL77m5dxnX3D6bS4a24+4xfUJSi4SG7oYgUWPWwjxuf30JRf7DMZUOXIXjlQW5B9rk7S7i9teXAAQ1eA/ddn1tJxB/mLWEF774cRyeCucOTCvgGw8NPyBRY8qclQfCdb/qTvQtKqtgypyV9b7t+thOIF768vujmi/RSeEuUeNohiMI9k1ADrftUAyRUHGYa1cON1+ik8Jdokab9MSA23oM3lyUF7QrWw+37aOpKVi8Vv2FXIebL9FJ4S5RY/Ko7iTGeg+aF+sxYg+5ajU+xkPmMUn8ZsYiJjw7n9xd++pl24mxXiaP6l7ndR+tS4a2O6r5Ep0U7hI1xgxoy73n96FteiIGtE1PZMqF/Zgytt9B8+6/oC8f3jKCP/z8OD5bu4PT//oxT3y8lrKKyqBu+97z+4TkbJm7x/Th8uPbH9hT95px+fHt9WVqI6OzZSSqjBnQttpArW7ehJM6Mbp3K+56ayl/nr2C1xbk8cdze3F8p6ZB3XYo3D2mD3eP6UN2djZrLxsR6nIkBLTnLo1a5jFJPPXLwTwxPovCknIufuILbnppITuLa78XLxIOtOcuApzRqxUndW3O1I/W8thHa3m3spLvvKu47pROJMXpv4lEHu25i/glxnn53end+OB3p9C/hZeHPljNyAeymZmzkQrdt1UijMJd5BDtMpL4df8EXpk0jNZpidz62hLOfOhj3lv2g24KIhFD4S5yGIM7ZPDGr0/g0csGUlbhuPa5+Yx59DM+Xb1dIS9hT+EucgRmxll9WvPeb0/m/gv6sC2/mMunfcm4xz9n3hqFvIQvhbtIAGK8Hi4a3J7/3DKC/z23F9/vLOKyp77kgqmf8Z8VOlwj4UfhLnIUEmK9XDGsA9mTR/Cnc3vxQ34JV0+fz1kPf8obC3PrdCGUSDAp3EVqISHWy3h/yD9wYT/KKir57cxvOPn/PuTxj9ayZ19ZqEuURk4n8IrUQazXw9isTM4f0JbsVVt54uN13PvvFTz4/mouyGrLFcM60K1laqjLlEZI4S4SBB6PcWqPlpzaoyVLN+1h+rwNvDw/lxe+2MjQjhlcfvyxnNGrJfEx3ppXJhIECneRIOvVJo0pF/bj9rOO4+X53/PCF99x40sLyUiO44KBbRk3qB1dtTcv9UzhLlJPMpLjmHRKZyae1IlP12znpa828sy8DTz5yXr6ZaYxNiuTn/dtQ0ZyXKhLlSikcBepZx6PcXK35pzcrTnbC0uYtTCPVxfkcsebS/nj28sY0b05Z/drw8+Oa0lyvP5LSnDoX5JIA2qWEs+Ekzox4aROLN+cz6yFeby5aBPvL99KQqyHU3u04MzerRnZowUpCnqpA/3rEQmR41o34bjWTbh1dA/mf7eLt7/ZxL+/3cLsJVuIi/FwUpdmnN6zJacd15LmqfGhLlcijMJdJMQ8HmNIxwyGdMzgrnN6seC7Xcxespn3lv3AByu2YraEvpnpnNajBSO6N6d3mzQ8Ht0PVY5M4S4SRrxVgv7Os3uyfHMBHyz3hfzf3l/FX99bRUZyHCd2acaJXZtxYpc9/m12AAANA0lEQVRmIbkJt4Q/hbtImDIzerZpQs82TbjxtK5sLyzh09XbyV65lU/XbOetbzYB0LFZMsd3yuD4Tk0Z0jGD1mkKe1G4i0SMZinxB+7T6pxj5Q8FfLp6O5+v3cE732zmpa++B6BdRiKDj81g4LHH4Aoqqah0eHUYp9FRuItEIDOjR6sm9GjVhAkndaK8opLlmwv4asNOvlq/g49Xb+P1hXkA3Jczh76Z6fRrl06/zDT6tkunTVoCZgr8aKZwF4kCMV4PfTLT6JOZxjUndsQ5x8ad+3jh3c8oSWnNwo27mfbpOsoqfEMTH5MUS++2afRs7Tvsc1zrJnRslkysV2MJRguFu0gUMjOObZrM8LaxjBjRG4CS8gpWbC5gce5ulm7K59tNe3hm3gZK/cMUx3k9dGqeTPdWqXRtkULXlql0aZFC+4wkhX4EUriLNBLxMV7foZl26QfmlVVUsm7bXpZvzmfFlgJWbMln/oZdvLlo04E2sV6jfUYSHZul0Kl5Msc2TaJj02TaN02idVqijueHKYW7SCMW6/XQvVUq3VsdPJBZYUk5a7YWsnZrIWu2FbJ+217Wb9/Lx6u3UVpeWWV5o216Iu0yksg8JonMYxJpm55I22MSaZOeSMvUeGK01x8SCncR+YmU+Bj6t0unf5W9fIDKSseW/GI2bN/Lhh37+H7XPjbu2Efurn3M3bSFHXtLD2rvMWiRmkCrtARaNfE9tmgST4vUBFqkxtOiSTzNUuLJSIrThVlBpnAXkYB5PEabdN9e+Qldfvr6vtJyNu0uIndXEZv3FLN5dxGb9hTzQ34xa7YVMm/tdgqKy3+ynNdjZCTH0TQ5jmYp8TRNiSMjOY6MpDgyUuI4JimO73ZU0HJzPsckxZGWGEtCrEdn/ByBwl1EgiYpLoYuLVLp0uLw49XvKy1na34JWwtK2FpQzPaCErYXlrKtoIQde0vZXljCxp372LW3lIKSgz8I7s/55MDzOK+HtKRYmiTEkJYYS5PEWJokxJKaEEPqgccYUuJ//En2//iee0mKi4na7wwCCnczGw08BHiBp5xz9x3yuvlfPwvYB1zpnPs6yLWKRK1ZC/OYMmclm3YX0SY9kcmjuvPK/I3MW7vzQJvhnTO4cFD7n7QDfjJv/nc7eenL77m5dxnX3D6bS4a24+4xfQLa7pgBbQ87P5Dl92+7wjm8Zj/ZdlJcDB2axbDo+9019uXOs3tycrfm7NxXyofzcujQrSe79pXx2drtZK/cxraCEgqKy/B6jLIKx4bte8kvLqeguOzAaZ81iY/xkBTnC/qkOC+JcV4SY70kxXlJiPU9T4jzkhDjJSHWQ0Lsj4/xMR7iY/yPsT8+j9v/4/3xebzXS2yMEev14FxgtdVFjeFuZl7gEeB0IBfIMbO3nHPLqjQ7E+jq/xkKTPU/ikgNZi3M4/bXl1BUVgFA3u4ibp656Cft5q3deVDY5+0uYvKr34CDskp3YN7vZi6isspyFc7xwhcbAQ4K2eq2e/vrS5j/3U5eW5D3k/nAQQFf3fJ12fbkV74B40Ao5+0u4o43l3Lv+X0YM6AtW5p6GdGnNbMW5vHB8q0Hli0uq+T7nUUH2u1XXFZBYUk5hcXlFBSXU1hSzt6ScvaWlrO3pIK9JeXsK61gb2k5+0p9z4tKKw487thbemC6pLyC4rJKisoqqKisezCf2TGWkSPrvJojCmTPfQiwxjm3DsDMZgDnAlXD/VzgOef7OPrCzNLNrLVzbnPQKxaJMlPmrDwQVEerur3TymraAbz05fcHBWx12y0qqziw133o/ClzVh4UntUtX5dtl1UTmoFut7p2vj1sL81SgjtccllFJcVlFZSUV1JS7nte6n/ue6ygpKyS0grf9IHH8krKKnw/nl0bg1pTdaymPw/MbCww2jk3wT89HhjqnLuhSpt3gPucc5/6pz8AbnXOzT9kXROBif7J7sDKWtbdDNhey2XDjfoSnhqsL3GtumTV17or9u3Bm5R2YLp0y5oFddluXZYPwrLNgO1HWrbqNsJcXf59Heuca15To0D23Kv7tuHQT4RA2uCcewJ4IoBtHrkgs/nOuUF1XU84UF/CU7T0xczml+/ZGvH9gOh5T6Bh+hLI1QW5QLsq05nAplq0ERGRBhJIuOcAXc2so5nFARcDbx3S5i3gCvM5Htij4+0iIqFT42EZ51y5md0AzMF3KuTTzrmlZjbJ//pjwGx8p0GuwXcq5FX1VzIQhEM7YUR9CU/R0pdo6QeoL0elxi9URUQk8mhEHxGRKKRwFxGJQmEf7maWYGZfmdk3ZrbUzP7on59hZu+Z2Wr/4zGhrjUQZuY1s4X+awMiuR8bzGyJmS0ys/n+eZHal3Qze9XMVpjZcjMbFol9MbPu/vdj/0++md0coX35rf//+7dm9pI/ByKuHwBm9ht/P5aa2c3+efXel7APd6AEONU51w/oD4z2n5FzG/CBc64r8IF/OhL8BlheZTpS+wEw0jnXv8r5upHal4eAd51zPYB++N6fiOuLc26l//3oD2ThO7nhDSKsL2bWFrgJGOSc643vRI6LibB+AJhZb+BafFf69wN+YWZdaYi+OOci5gdIAr7GN27NSqC1f35rYGWo6wug/kz/G3kq8I5/XsT1w1/rBqDZIfMiri9AE2A9/pMLIrkvh9R/BjAvEvsCtAW+BzLwndH3jr8/EdUPf50X4htscf/0HcDvG6IvkbDnvv9QxiJgK/Cec+5LoKXzn0vvf2wRyhoD9CC+N7bqEByR2A/wXYE818wW+IeVgMjsSydgG/CM/3DZU2aWTGT2paqLgZf8zyOqL865POABYCOwGd91M3OJsH74fQucbGZNzSwJ3ynj7WiAvkREuDvnKpzvT81MYIj/T52IYma/ALY65yJl7IuaDHfODcQ3Iuj1ZnZyqAuqpRhgIDDVOTcA2EsE/Ll/JP6LDc8BXgl1LbXhP/58LtARaAMkm9nloa2qdpxzy4H7gfeAd4FvgJ/eraQeRES47+ec2w1kA6OBH8ysNYD/cWsISwvEcOAcM9sAzABONbMXiLx+AOCc2+R/3IrvuO4QIrMvuUCu/69BgFfxhX0k9mW/M4GvnXM/+KcjrS8/A9Y757Y558qA14ETiLx+AOCcm+acG+icOxnYCaymAfoS9uFuZs3NLN3/PBHfG78C35AHv/Q3+yXwZmgqDIxz7nbnXKZzrgO+P5n/45y7nAjrB4CZJZtZ6v7n+I6HfksE9sU5twX43sy6+2edhm8464jrSxWX8OMhGYi8vmwEjjezJDMzfO/JciKvHwCYWQv/Y3vgfHzvTb33JeyvUDWzvsCz+L4x9wAvO+f+18yaAi8D7fH9Y7jQObfz8GsKH2Y2ArjFOfeLSOyHmXXCt7cOvsMaLzrn7onEvgCYWX/gKSAOWIdv+AwPkdmXJHxfRnZyzu3xz4u498V/yvNF+A5hLAQmAClEWD8AzOwToClQBvzOOfdBQ7wnYR/uIiJy9ML+sIyIiBw9hbuISBRSuIuIRCGFu4hIFFK4i4hEoUBukC3SoPyniX3gn2wFVOAbIgBgiHOuNCSFHYGZXQ3M9p83LxJyOhVSwpqZ3QUUOuceCINavM65isO89ilwg3Nu0VGsL8Y51yCXokvjo8MyElHM7JfmG99/kZk9amYeM4sxs91mNsXMvjazOWY21Mw+MrN1ZnaWf9kJZvaG//WVZvaHANd7t5l9hW9coz+aWY5/fO7HzOcifMNRz/QvH2dmuVWurD7ezN73P7/bzB43s/fwDVYWY2Z/9W97sZlNaPjfqkQjhbtEDP+AcecBJ/gHkovBN5QDQBow1z+YWSlwF77L1i8E/rfKaob4lxkIXGpm/QNY79fOuSHOuc+Bh5xzg4E+/tdGO+dmAouAi5xvPPWaDhsNAM52zo0HJuIbUG4IMBjfIGzta/P7EalKx9wlkvwMXwDO9w05QiK+S+0Bipxz7/mfL8E3TGy5mS0BOlRZxxzn3C4AM5sFnIjv/8Hh1lvKj0MtAJxmZpOBBKAZsAD491H2403nXLH/+RnAcWZW9cOkK75L0kVqTeEukcSAp51zdxw00ywGXwjvV4nvDl77n1f9d37ol0yuhvUWOf8XU/5xW/4BDHTO5ZnZ3fhCvjrl/PiX8aFt9h7Sp1875z5AJIh0WEYiyfvAODNrBr6zampxCOMM890zNQnfmOHzjmK9ifg+LLb7R8W8oMprBUBqlekN+G51xyHtDjUH+LX/g2T/fVATj7JPIj+hPXeJGM65Jf7RAt83Mw++UfYmAZuOYjWfAi8CnYHn95/dEsh6nXM7zOxZfMMbfwd8WeXlZ4CnzKwI33H9u4AnzWwL8NUR6nkc38iAi/yHhLbi+9ARqROdCimNhv9MlN7OuZtDXYtIfdNhGRGRKKQ9dxGRKKQ9dxGRKKRwFxGJQgp3EZEopHAXEYlCCncRkSj0/wHRUJwHFwSFegAAAABJRU5ErkJggg==\n", 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" ] }, "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", "data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n", "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n", "plt.grid(True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il y'a bien une incidence de la temperature avec un risque de panne estimatif de \\>0,81 pour une unetemerature de 31F.Cepdnant je ne connais pas l'intervale de confiance de ce que j'ai dans ce modèle statistique. Ce aui me derange c'est que je suis tres loin des condtions de mesures. Aussi il est difficile de verifier la conordance du modele ave le phenomene physique réel. Ca donne pas envie de monter dans la navette..." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set(color_codes=True)\n", "plt.xlim(30,90)\n", "plt.ylim(0,1)\n", "sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mon interval de confiance varie entre 0 et 1 ce aui confirme que mon modèle n'est pas fiable. Je ne peux pas considerer les elements de sortie du modèle." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false, "scrolled": true }, "source": [ "Comme on pouvait s'attendre au vu des données initiales, la\n", "température n'a pas d'impact notable sur la probabilité d'échec des\n", "joints toriques. Elle sera d'environ 0.2, comme dans les essais\n", "précédents où nous il y a eu défaillance d'au moins un joint. Revenons\n", "à l'ensemble des données initiales pour estimer la probabilité de\n", "défaillance d'un joint:\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.06521739130434782\n" ] } ], "source": [ "data = pd.read_csv(\"shuttle.csv\")\n", "print(np.sum(data.Malfunction)/np.sum(data.Count))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cette probabilité est donc d'environ $p=0.065$, sachant qu'il existe\n", "un joint primaire un joint secondaire sur chacune des trois parties du\n", "lançeur, la probabilité de défaillance des deux joints d'un lançeur\n", "est de $p^2 \\approx 0.00425$. La probabilité de défaillance d'un des\n", "lançeur est donc de $1-(1-p^2)^3 \\approx 1.2%$. Ça serait vraiment\n", "pas de chance... Tout est sous contrôle, le décollage peut donc avoir\n", "lieu demain comme prévu.\n", "\n", "Seulement, le lendemain, la navette Challenger explosera et emportera\n", "avec elle ses sept membres d'équipages. L'opinion publique est\n", "fortement touchée et lors de l'enquête qui suivra, la fiabilité des\n", "joints toriques sera directement mise en cause. Au delà des problèmes\n", "de communication interne à la NASA qui sont pour beaucoup dans ce\n", "fiasco, l'analyse précédente comporte (au moins) un petit\n", "problème... Saurez-vous le trouver ? Vous êtes libre de modifier cette\n", "analyse et de regarder ce jeu de données sous tous les angles afin\n", "d'expliquer ce qui ne va pas." ] } ], "metadata": { "celltoolbar": "Hide code", "kernelspec": { "display_name": "Python 3", "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }