Merge branch 'master' of...

Merge branch 'master' of https://app-learninglab.inria.fr/moocrr/gitlab/28e1f4de90ce29a7aec6f2ae83fdfffc/mooc-rr

Merge branch 'master' of...
Merge branch 'master' of https://app-learninglab.inria.fr/moocrr/gitlab/28e1f4de90ce29a7aec6f2ae83fdfffc/mooc-rr
5e8050b5 · Rémi Falcand · 25a5295c · 41468824 · 5e8050b5 · 5e8050b5
Commit 5e8050b5 authored Sep 27, 2025 by Rémi Falcand
9 changed files
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
@@ -4,7 +4,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "# Premier test jupyter"
+    "# À propos du calcul de $\\pi$"
   ]
  },
  {
@@ -24,6 +24,136 @@
    "import os\n",
    "print('hello world')"
   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## En demandant à la lib maths\n",
+    "Mon ordinateur m'indique que $\\pi$ vaut *approximativement*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.141592653589793\n"
+     ]
+    }
+   ],
+   "source": [
+    "from math import *\n",
+    "print(pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## En utilisant la méthode des aiguilles de Buffon\n",
+    "Mais calculé avec la __méthode__ des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme __approximation__ :\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.128911138923655"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "np.random.seed(seed=42)\n",
+    "N = 10000\n",
+    "x = np.random.uniform(size=N, low=0, high=1)\n",
+    "theta = np.random.uniform(size=N, low=0, high=pi/2)\n",
+    "2/(sum((x+np.sin(theta))>1)/N)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Avec un argument \"fréquentiel\" de surface\n",
+    "Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d'appel à la fonction sinus se base sur le fait que  si $X\\sim U(0,1)$ et $Y\\sim U(0,1)$ alors $P[X^2+Y^2\\leq 1] = \\pi/4$ (voir [méthode de Monte Carlo sur Wikipedia](https://fr.wikipedia.org/wiki/M%C3%A9thode_de_Monte-Carlo#D%C3%A9termination_de_la_valeur_de_%CF%80)). Le code suivant illustre ce fait :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline  \n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "np.random.seed(seed=42)\n",
+    "N = 1000\n",
+    "x = np.random.uniform(size=N, low=0, high=1)\n",
+    "y = np.random.uniform(size=N, low=0, high=1)\n",
+    "\n",
+    "accept = (x*x+y*y) <= 1\n",
+    "reject = np.logical_not(accept)\n",
+    "\n",
+    "fig, ax = plt.subplots(1)\n",
+    "ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)\n",
+    "ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)\n",
+    "ax.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il est alors aisé d'obtenir une approximation (pas terrible) de $\\pi$ en comptant combien de fois, en moyenne, $X^2 + Y^2$ est inférieur à 1 :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.112"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4*np.mean(accept)"
+   ]
  }
 ],
 "metadata": {

--- a/module2/exo2/exercice.ipynb
+++ b/module2/exo2/exercice.ipynb
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculer la moyenne et l'écart-type, le min, la médiane et le max des données suivantes :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "moyenne    : 14.113000000000001\n",
+      "écart-type : 4.334094455301447\n",
+      "min        : 2.8\n",
+      "médiane    : 14.5\n",
+      "max         : 23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "s = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2,\n",
+    "     14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, \n",
+    "     15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2,\n",
+    "     16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9,\n",
+    "     16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7,\n",
+    "     17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,\n",
+    "     19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1,\n",
+    "     19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9,\n",
+    "     20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2,\n",
+    "     20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
+    "\n",
+    "print ('moyenne    :', np.mean(s))\n",
+    "print ('écart-type :', np.std(s,ddof = 1))\n",
+    "print ('min        :', np.min(s))\n",
+    "print ('médiane    :', np.median(s))\n",
+    "print('max         :', np.max(s))"
+   ]
+  }
+ ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
@@ -16,10 +63,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/module2/exo3/exercice.ipynb
+++ b/module2/exo3/exercice.ipynb
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Réaliser un affichage graphique\n",
+    "Réaliser un affichage graphique (séquence plot + histogramme) des données de l'exercice précédent. Voir les images attendues ci-dessous :\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    " %matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "\n",
+    "s = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2,\n",
+    "     14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, \n",
+    "     15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2,\n",
+    "     16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9,\n",
+    "     16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7,\n",
+    "     17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,\n",
+    "     19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1,\n",
+    "     19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9,\n",
+    "     20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2,\n",
+    "     20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
+    "\n",
+    "#plt.style.use('_mpl-gallery')\n",
+    "# plot:\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.grid(True)\n",
+    "ax.axis([0, 100, 0, 25])\n",
+    "ax.plot(s)\n",
+    "plt.show()\n",
+    "\n",
+    "fig2, ax2 = plt.subplots()\n",
+    "ax2.grid(True)\n",
+    "n, bins, patches = ax2.hist(s, 10,  facecolor='C0', alpha=0.75)\n",
+    "ax2.axis([0, 25, 0, 25])\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# préparation du document computationnel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(data=s)\n",
+    "df.to_csv('./exo4_donnes_brutes.csv', index = False)\n"
+   ]
+  }
+ ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
@@ -16,10 +106,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/module2/exo3/exo4_donnes_brutes.csv
+++ b/module2/exo3/exo4_donnes_brutes.csv
+0
+14.0
+7.6
+11.2
+12.8
+12.5
+9.9
+14.9
+9.4
+16.9
+10.2
+14.9
+18.1
+7.3
+9.8
+10.9
+12.2
+9.9
+2.9
+2.8
+15.4
+15.7
+9.7
+13.1
+13.2
+12.3
+11.7
+16.0
+12.4
+17.9
+12.2
+16.2
+18.7
+8.9
+11.9
+12.1
+14.6
+12.1
+4.7
+3.9
+16.9
+16.8
+11.3
+14.4
+15.7
+14.0
+13.6
+18.0
+13.6
+19.9
+13.7
+17.0
+20.5
+9.9
+12.5
+13.2
+16.1
+13.5
+6.3
+6.4
+17.6
+19.1
+12.8
+15.5
+16.3
+15.2
+14.6
+19.1
+14.4
+21.4
+15.1
+19.6
+21.7
+11.3
+15.0
+14.3
+16.8
+14.0
+6.8
+8.2
+19.9
+20.4
+14.6
+16.4
+18.7
+16.8
+15.8
+20.4
+15.8
+22.4
+16.2
+20.3
+23.4
+12.1
+15.5
+15.4
+18.4
+15.7
+10.2
+8.9
+21.0
--- a/module2/exo4/exercice.ipynb
+++ b/module2/exo4/exercice.ipynb
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "source": [
+    "# Journal sur les exercices de documents computationnel\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "source": [
+    "<!-- les données sont dans ../exo3/exo4_donnes_brutes.csv -->\n",
+    "Lecture des données et affichage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "        0\n",
+      "0     0.0\n",
+      "1    14.0\n",
+      "2     7.6\n",
+      "3    11.2\n",
+      "4    12.8\n",
+      "5    12.5\n",
+      "6     9.9\n",
+      "7    14.9\n",
+      "8     9.4\n",
+      "9    16.9\n",
+      "10   10.2\n",
+      "11   14.9\n",
+      "12   18.1\n",
+      "13    7.3\n",
+      "14    9.8\n",
+      "15   10.9\n",
+      "16   12.2\n",
+      "17    9.9\n",
+      "18    2.9\n",
+      "19    2.8\n",
+      "20   15.4\n",
+      "21   15.7\n",
+      "22    9.7\n",
+      "23   13.1\n",
+      "24   13.2\n",
+      "25   12.3\n",
+      "26   11.7\n",
+      "27   16.0\n",
+      "28   12.4\n",
+      "29   17.9\n",
+      "..    ...\n",
+      "71   19.6\n",
+      "72   21.7\n",
+      "73   11.3\n",
+      "74   15.0\n",
+      "75   14.3\n",
+      "76   16.8\n",
+      "77   14.0\n",
+      "78    6.8\n",
+      "79    8.2\n",
+      "80   19.9\n",
+      "81   20.4\n",
+      "82   14.6\n",
+      "83   16.4\n",
+      "84   18.7\n",
+      "85   16.8\n",
+      "86   15.8\n",
+      "87   20.4\n",
+      "88   15.8\n",
+      "89   22.4\n",
+      "90   16.2\n",
+      "91   20.3\n",
+      "92   23.4\n",
+      "93   12.1\n",
+      "94   15.5\n",
+      "95   15.4\n",
+      "96   18.4\n",
+      "97   15.7\n",
+      "98   10.2\n",
+      "99    8.9\n",
+      "100  21.0\n",
+      "\n",
+      "[101 rows x 1 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "df = pd.read_csv ('../exo3/exo4_donnes_brutes.csv',index_col=None, header=None)\n",
+    "\n",
+    "print (type(df))\n",
+    "print (df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Premier graphique\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.grid(True)\n",
+    "ax.axis([0, 100, 0, 25])\n",
+    "ax.plot(df)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "second graphique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig2, ax2 = plt.subplots()\n",
+    "ax2.grid(True)\n",
+    "n, bins, patches = ax2.hist(np.array(df), 10,  facecolor='C0')\n",
+    "ax2.axis([0, 25, 0, 25])\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Le second graphique a imposer de changer le type de données passé à la fonction historique via np.array . L'import des données se faisant via pandas ne fonctionnant pas avec cette fonction, bien que fonctionnant avec plot."
+   ]
+  }
+ ],
 "metadata": {
+  "hide_code_all_hidden": true,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
@@ -16,10 +217,9 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/module2/exo4/exo4_donnees.csv
+++ b/module2/exo4/exo4_donnees.csv
+Valeur
+14.0
+7.6
+11.2
+12.8
+12.5
+9.9
+14.9
+9.4
+16.9
+10.2
+14.9
+18.1
+7.3
+9.8
+10.9
+12.2
+9.9
+2.9
+2.8
+15.4
+     15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2,
+     16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9,
+     16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7,
+     17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,
+     19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1,
+     19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9,
+     20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2,
+     20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0
\ No newline at end of file
--- a/module2/exo5/exo5_fr.ipynb
+++ b/module2/exo5/exo5_fr.ipynb
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!-- 2025/08/13 début du travail sur cet exercice -->"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
@@ -261,30 +268,30 @@
       "</div>"
      ],
      "text/plain": [
-       "         Date  Count  Temperature  Pressure  Malfunction\n",
+       "        Date  Count  Temperature  Pressure  Malfunction\n",
-       "0     4/12/81      6           66        50            0\n",
+       "0    4/12/81      6           66        50            0\n",
-       "1    11/12/81      6           70        50            1\n",
+       "1   11/12/81      6           70        50            1\n",
-       "2     3/22/82      6           69        50            0\n",
+       "2    3/22/82      6           69        50            0\n",
-       "3    11/11/82      6           68        50            0\n",
+       "3   11/11/82      6           68        50            0\n",
-       "4     4/04/83      6           67        50            0\n",
+       "4    4/04/83      6           67        50            0\n",
-       "5     6/18/82      6           72        50            0\n",
+       "5    6/18/82      6           72        50            0\n",
-       "6     8/30/83      6           73       100            0\n",
+       "6    8/30/83      6           73       100            0\n",
-       "7    11/28/83      6           70       100            0\n",
+       "7   11/28/83      6           70       100            0\n",
-       "8     2/03/84      6           57       200            1\n",
+       "8    2/03/84      6           57       200            1\n",
-       "9     4/06/84      6           63       200            1\n",
+       "9    4/06/84      6           63       200            1\n",
-       "10    8/30/84      6           70       200            1\n",
+       "10   8/30/84      6           70       200            1\n",
-       "11   10/05/84      6           78       200            0\n",
+       "11  10/05/84      6           78       200            0\n",
-       "12   11/08/84      6           67       200            0\n",
+       "12  11/08/84      6           67       200            0\n",
-       "13    1/24/85      6           53       200            2\n",
+       "13   1/24/85      6           53       200            2\n",
-       "14    4/12/85      6           67       200            0\n",
+       "14   4/12/85      6           67       200            0\n",
-       "15    4/29/85      6           75       200            0\n",
+       "15   4/29/85      6           75       200            0\n",
-       "16    6/17/85      6           70       200            0\n",
+       "16   6/17/85      6           70       200            0\n",
-       "17  7/29/85      6           81       200            0\n",
+       "17   7/29/85      6           81       200            0\n",
-       "18    8/27/85      6           76       200            0\n",
+       "18   8/27/85      6           76       200            0\n",
-       "19   10/03/85      6           79       200            0\n",
+       "19  10/03/85      6           79       200            0\n",
-       "20   10/30/85      6           75       200            2\n",
+       "20  10/30/85      6           75       200            2\n",
-       "21   11/26/85      6           76       200            0\n",
+       "21  11/26/85      6           76       200            0\n",
-       "22    1/12/86      6           58       200            1"
+       "22   1/12/86      6           58       200            1"
      ]
     },
     "execution_count": 1,
@@ -317,12 +324,14 @@
    "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"
+    "moins un joint a été défectueux.\n",
+    "\n",
+    "* secon test avec uniquement les tempéraures <65"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
@@ -355,14 +364,6 @@
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>11/12/81</td>\n",
-       "      <td>6</td>\n",
-       "      <td>70</td>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2/03/84</td>\n",
       "      <td>6</td>\n",
@@ -379,14 +380,6 @@
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>8/30/84</td>\n",
-       "      <td>6</td>\n",
-       "      <td>70</td>\n",
-       "      <td>200</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1/24/85</td>\n",
       "      <td>6</td>\n",
@@ -395,14 +388,6 @@
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>10/30/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>75</td>\n",
-       "      <td>200</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1/12/86</td>\n",
       "      <td>6</td>\n",
@@ -415,24 +400,22 @@
       "</div>"
      ],
      "text/plain": [
-       "        Date  Count  Temperature  Pressure  Malfunction\n",
+       "       Date  Count  Temperature  Pressure  Malfunction\n",
-       "1   11/12/81      6           70        50            1\n",
+       "8   2/03/84      6           57       200            1\n",
-       "8    2/03/84      6           57       200            1\n",
+       "9   4/06/84      6           63       200            1\n",
-       "9    4/06/84      6           63       200            1\n",
+       "13  1/24/85      6           53       200            2\n",
-       "10   8/30/84      6           70       200            1\n",
+       "22  1/12/86      6           58       200            1"
-       "13   1/24/85      6           53       200            2\n",
-       "20  10/30/85      6           75       200            2\n",
-       "22   1/12/86      6           58       200            1"
      ]
     },
-     "execution_count": 2,
+     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
-    "data = data[data.Malfunction>0]\n",
+    "#data = data[data.Malfunction>0]    data.Temperature <= 65\n",
-    "data"
+    "data2 = data[data.Temperature <= 65]\n",
+    "data2"
   ]
  },
  {
@@ -448,12 +431,12 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
@@ -469,8 +452,8 @@
    "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",
+    "data2[\"Frequency\"]=data2.Malfunction/data2.Count\n",
-    "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
+    "data2.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
    "plt.grid(True)"
   ]
  },
@@ -500,7 +483,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
@@ -509,10 +492,10 @@
       "<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>     7</td>  \n",
+       "  <th>Dep. Variable:</th>      <td>Frequency</td>    <th>  No. Observations:  </th>  <td>     4</td>  \n",
       "</tr>\n",
       "<tr>\n",
-       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>     5</td>  \n",
+       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>     2</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>     1</td>  \n",
@@ -521,13 +504,13 @@
       "  <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> -2.5250</td> \n",
+       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -1.3845</td> \n",
       "</tr>\n",
       "<tr>\n",
-       "  <th>Date:</th>           <td>Sat, 13 Apr 2019</td> <th>  Deviance:          </th> <td> 0.22231</td> \n",
+       "  <th>Date:</th>           <td>Mon, 18 Aug 2025</td> <th>  Deviance:          </th> <td>0.040847</td> \n",
       "</tr>\n",
       "<tr>\n",
-       "  <th>Time:</th>               <td>19:11:24</td>     <th>  Pearson chi2:      </th>  <td> 0.236</td>  \n",
+       "  <th>Time:</th>               <td>15:47:24</td>     <th>  Pearson chi2:      </th>  <td>0.0407</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>4</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
@@ -538,10 +521,10 @@
       "       <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>   -1.3895</td> <td>    7.828</td> <td>   -0.178</td> <td> 0.859</td> <td>  -16.732</td> <td>   13.953</td>\n",
+       "  <th>Intercept</th>   <td>    4.3201</td> <td>   20.789</td> <td>    0.208</td> <td> 0.835</td> <td>  -36.425</td> <td>   45.066</td>\n",
       "</tr>\n",
       "<tr>\n",
-       "  <th>Temperature</th> <td>    0.0014</td> <td>    0.122</td> <td>    0.012</td> <td> 0.991</td> <td>   -0.238</td> <td>    0.240</td>\n",
+       "  <th>Temperature</th> <td>   -0.0985</td> <td>    0.364</td> <td>   -0.271</td> <td> 0.787</td> <td>   -0.812</td> <td>    0.615</td>\n",
       "</tr>\n",
       "</table>"
      ],
@@ -550,24 +533,24 @@
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
-       "Dep. Variable:              Frequency   No. Observations:                    7\n",
+       "Dep. Variable:              Frequency   No. Observations:                    4\n",
-       "Model:                            GLM   Df Residuals:                        5\n",
+       "Model:                            GLM   Df Residuals:                        2\n",
       "Model Family:                Binomial   Df Model:                            1\n",
       "Link Function:                  logit   Scale:                          1.0000\n",
-       "Method:                          IRLS   Log-Likelihood:                -2.5250\n",
+       "Method:                          IRLS   Log-Likelihood:                -1.3845\n",
-       "Date:                Sat, 13 Apr 2019   Deviance:                      0.22231\n",
+       "Date:                Mon, 18 Aug 2025   Deviance:                     0.040847\n",
-       "Time:                        19:11:24   Pearson chi2:                    0.236\n",
+       "Time:                        15:47:24   Pearson chi2:                   0.0407\n",
       "No. Iterations:                     4   Covariance Type:             nonrobust\n",
       "===============================================================================\n",
       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
-       "Intercept      -1.3895      7.828     -0.178      0.859     -16.732      13.953\n",
+       "Intercept       4.3201     20.789      0.208      0.835     -36.425      45.066\n",
-       "Temperature     0.0014      0.122      0.012      0.991      -0.238       0.240\n",
+       "Temperature    -0.0985      0.364     -0.271      0.787      -0.812       0.615\n",
       "===============================================================================\n",
       "\"\"\""
      ]
     },
-     "execution_count": 4,
+     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -575,10 +558,10 @@
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
-    "data[\"Success\"]=data.Count-data.Malfunction\n",
+    "data2[\"Success\"]=data2.Count-data2.Malfunction\n",
-    "data[\"Intercept\"]=1\n",
+    "data2[\"Intercept\"]=1\n",
    "\n",
-    "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
+    "logmodel=sm.GLM(data2['Frequency'], data2[['Intercept','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
    "\n",
    "logmodel.summary()"
   ]
@@ -605,12 +588,120 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Count</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Pressure</th>\n",
+       "      <th>Malfunction</th>\n",
+       "      <th>Frequency</th>\n",
+       "      <th>Success</th>\n",
+       "      <th>Intercept</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2/03/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>57</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4/06/84</td>\n",
+       "      <td>6</td>\n",
+       "      <td>63</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1/24/85</td>\n",
+       "      <td>6</td>\n",
+       "      <td>53</td>\n",
+       "      <td>200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1/12/86</td>\n",
+       "      <td>6</td>\n",
+       "      <td>58</td>\n",
+       "      <td>200</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Date  Count  Temperature  Pressure  Malfunction  Frequency  Success  \\\n",
+       "8   2/03/84      6           57       200            1   0.166667        5   \n",
+       "9   4/06/84      6           63       200            1   0.166667        5   \n",
+       "13  1/24/85      6           53       200            2   0.333333        4   \n",
+       "22  1/12/86      6           58       200            1   0.166667        5   \n",
+       "\n",
+       "    Intercept  \n",
+       "8           1  \n",
+       "9           1  \n",
+       "13          1  \n",
+       "22          1  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
@@ -623,13 +714,492 @@
   ],
   "source": [
    "%matplotlib inline\n",
-    "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
+    "data_pred = pd.DataFrame({'Temperature': np.linspace(start=25, stop=60, 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.scatter(x=data2[\"Temperature\"],y=data2[\"Frequency\"])\n",
    "plt.grid(True)"
   ]
  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Intercept</th>\n",
+       "      <th>Temperature</th>\n",
+       "      <th>Frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>0.864905</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>25.291667</td>\n",
+       "      <td>0.861511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>25.583333</td>\n",
+       "      <td>0.858046</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>25.875000</td>\n",
+       "      <td>0.854509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>26.166667</td>\n",
+       "      <td>0.850900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>26.458333</td>\n",
+       "      <td>0.847216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>26.750000</td>\n",
+       "      <td>0.843459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>27.041667</td>\n",
+       "      <td>0.839627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>27.333333</td>\n",
+       "      <td>0.835719</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>27.625000</td>\n",
+       "      <td>0.831734</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>27.916667</td>\n",
+       "      <td>0.827674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1</td>\n",
+       "      <td>28.208333</td>\n",
+       "      <td>0.823536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1</td>\n",
+       "      <td>28.500000</td>\n",
+       "      <td>0.819320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1</td>\n",
+       "      <td>28.791667</td>\n",
+       "      <td>0.815026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1</td>\n",
+       "      <td>29.083333</td>\n",
+       "      <td>0.810654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1</td>\n",
+       "      <td>29.375000</td>\n",
+       "      <td>0.806203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1</td>\n",
+       "      <td>29.666667</td>\n",
+       "      <td>0.801673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1</td>\n",
+       "      <td>29.958333</td>\n",
+       "      <td>0.797064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1</td>\n",
+       "      <td>30.250000</td>\n",
+       "      <td>0.792375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1</td>\n",
+       "      <td>30.541667</td>\n",
+       "      <td>0.787607</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>1</td>\n",
+       "      <td>30.833333</td>\n",
+       "      <td>0.782759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1</td>\n",
+       "      <td>31.125000</td>\n",
+       "      <td>0.777832</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>1</td>\n",
+       "      <td>31.416667</td>\n",
+       "      <td>0.772826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>1</td>\n",
+       "      <td>31.708333</td>\n",
+       "      <td>0.767741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>1</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>0.762576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1</td>\n",
+       "      <td>32.291667</td>\n",
+       "      <td>0.757333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1</td>\n",
+       "      <td>32.583333</td>\n",
+       "      <td>0.752012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>1</td>\n",
+       "      <td>32.875000</td>\n",
+       "      <td>0.746614</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>1</td>\n",
+       "      <td>33.166667</td>\n",
+       "      <td>0.741138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>1</td>\n",
+       "      <td>33.458333</td>\n",
+       "      <td>0.735586</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>1</td>\n",
+       "      <td>51.541667</td>\n",
+       "      <td>0.318910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>1</td>\n",
+       "      <td>51.833333</td>\n",
+       "      <td>0.312700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>1</td>\n",
+       "      <td>52.125000</td>\n",
+       "      <td>0.306557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>94</th>\n",
+       "      <td>1</td>\n",
+       "      <td>52.416667</td>\n",
+       "      <td>0.300481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>1</td>\n",
+       "      <td>52.708333</td>\n",
+       "      <td>0.294475</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>1</td>\n",
+       "      <td>53.000000</td>\n",
+       "      <td>0.288539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>1</td>\n",
+       "      <td>53.291667</td>\n",
+       "      <td>0.282675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>1</td>\n",
+       "      <td>53.583333</td>\n",
+       "      <td>0.276884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>1</td>\n",
+       "      <td>53.875000</td>\n",
+       "      <td>0.271166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>1</td>\n",
+       "      <td>54.166667</td>\n",
+       "      <td>0.265524</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>1</td>\n",
+       "      <td>54.458333</td>\n",
+       "      <td>0.259956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>1</td>\n",
+       "      <td>54.750000</td>\n",
+       "      <td>0.254466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>1</td>\n",
+       "      <td>55.041667</td>\n",
+       "      <td>0.249052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>1</td>\n",
+       "      <td>55.333333</td>\n",
+       "      <td>0.243715</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>1</td>\n",
+       "      <td>55.625000</td>\n",
+       "      <td>0.238457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>1</td>\n",
+       "      <td>55.916667</td>\n",
+       "      <td>0.233277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>1</td>\n",
+       "      <td>56.208333</td>\n",
+       "      <td>0.228176</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>108</th>\n",
+       "      <td>1</td>\n",
+       "      <td>56.500000</td>\n",
+       "      <td>0.223154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>109</th>\n",
+       "      <td>1</td>\n",
+       "      <td>56.791667</td>\n",
+       "      <td>0.218211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>1</td>\n",
+       "      <td>57.083333</td>\n",
+       "      <td>0.213348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111</th>\n",
+       "      <td>1</td>\n",
+       "      <td>57.375000</td>\n",
+       "      <td>0.208564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>112</th>\n",
+       "      <td>1</td>\n",
+       "      <td>57.666667</td>\n",
+       "      <td>0.203859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>1</td>\n",
+       "      <td>57.958333</td>\n",
+       "      <td>0.199234</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>114</th>\n",
+       "      <td>1</td>\n",
+       "      <td>58.250000</td>\n",
+       "      <td>0.194689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115</th>\n",
+       "      <td>1</td>\n",
+       "      <td>58.541667</td>\n",
+       "      <td>0.190222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>116</th>\n",
+       "      <td>1</td>\n",
+       "      <td>58.833333</td>\n",
+       "      <td>0.185834</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>117</th>\n",
+       "      <td>1</td>\n",
+       "      <td>59.125000</td>\n",
+       "      <td>0.181525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>118</th>\n",
+       "      <td>1</td>\n",
+       "      <td>59.416667</td>\n",
+       "      <td>0.177294</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119</th>\n",
+       "      <td>1</td>\n",
+       "      <td>59.708333</td>\n",
+       "      <td>0.173141</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>1</td>\n",
+       "      <td>60.000000</td>\n",
+       "      <td>0.169064</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>121 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Intercept  Temperature  Frequency\n",
+       "0            1    25.000000   0.864905\n",
+       "1            1    25.291667   0.861511\n",
+       "2            1    25.583333   0.858046\n",
+       "3            1    25.875000   0.854509\n",
+       "4            1    26.166667   0.850900\n",
+       "5            1    26.458333   0.847216\n",
+       "6            1    26.750000   0.843459\n",
+       "7            1    27.041667   0.839627\n",
+       "8            1    27.333333   0.835719\n",
+       "9            1    27.625000   0.831734\n",
+       "10           1    27.916667   0.827674\n",
+       "11           1    28.208333   0.823536\n",
+       "12           1    28.500000   0.819320\n",
+       "13           1    28.791667   0.815026\n",
+       "14           1    29.083333   0.810654\n",
+       "15           1    29.375000   0.806203\n",
+       "16           1    29.666667   0.801673\n",
+       "17           1    29.958333   0.797064\n",
+       "18           1    30.250000   0.792375\n",
+       "19           1    30.541667   0.787607\n",
+       "20           1    30.833333   0.782759\n",
+       "21           1    31.125000   0.777832\n",
+       "22           1    31.416667   0.772826\n",
+       "23           1    31.708333   0.767741\n",
+       "24           1    32.000000   0.762576\n",
+       "25           1    32.291667   0.757333\n",
+       "26           1    32.583333   0.752012\n",
+       "27           1    32.875000   0.746614\n",
+       "28           1    33.166667   0.741138\n",
+       "29           1    33.458333   0.735586\n",
+       "..         ...          ...        ...\n",
+       "91           1    51.541667   0.318910\n",
+       "92           1    51.833333   0.312700\n",
+       "93           1    52.125000   0.306557\n",
+       "94           1    52.416667   0.300481\n",
+       "95           1    52.708333   0.294475\n",
+       "96           1    53.000000   0.288539\n",
+       "97           1    53.291667   0.282675\n",
+       "98           1    53.583333   0.276884\n",
+       "99           1    53.875000   0.271166\n",
+       "100          1    54.166667   0.265524\n",
+       "101          1    54.458333   0.259956\n",
+       "102          1    54.750000   0.254466\n",
+       "103          1    55.041667   0.249052\n",
+       "104          1    55.333333   0.243715\n",
+       "105          1    55.625000   0.238457\n",
+       "106          1    55.916667   0.233277\n",
+       "107          1    56.208333   0.228176\n",
+       "108          1    56.500000   0.223154\n",
+       "109          1    56.791667   0.218211\n",
+       "110          1    57.083333   0.213348\n",
+       "111          1    57.375000   0.208564\n",
+       "112          1    57.666667   0.203859\n",
+       "113          1    57.958333   0.199234\n",
+       "114          1    58.250000   0.194689\n",
+       "115          1    58.541667   0.190222\n",
+       "116          1    58.833333   0.185834\n",
+       "117          1    59.125000   0.181525\n",
+       "118          1    59.416667   0.177294\n",
+       "119          1    59.708333   0.173141\n",
+       "120          1    60.000000   0.169064\n",
+       "\n",
+       "[121 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_pred\n"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {
@@ -638,30 +1208,48 @@
    "scrolled": true
   },
   "source": [
-    "Comme on pouvait s'attendre au vu des données initiales, la\n",
+    "<!-- 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"
+    "défaillance d'un joint: -->\n",
+    "\n",
+    "En ne prenant que la partie des température basse, on voit une influence notable de la baisse de température avec une probabilité d'échec de 0.77\n"
   ]
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "0.06521739130434782\n"
+      "data   p         :  0.06521739130434782\n",
+      "data   p^2       :  0.004253308128544423\n",
+      "data   1−(1−𝑝2)3 :  0.01270572944054793   soit 1.27%\n",
+      "\n",
+      "data2  p         :  0.20833333333333334\n",
+      "data2  p^2       :  0.04340277777777778\n",
+      "data2  1−(1−𝑝2)3 :  0.1246386921781899   soit 12.46%\n",
+      "\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv(\"shuttle.csv\")\n",
-    "print(np.sum(data.Malfunction)/np.sum(data.Count))"
+    "p_data = np.sum(data.Malfunction)/np.sum(data.Count)\n",
+    "p_data2 = np.sum(data2.Malfunction)/np.sum(data2.Count)\n",
+    "print(\"data   p         : \",p_data)\n",
+    "print('data   p^2       : ',p_data**2)\n",
+    "print('data   1−(1−𝑝2)3 : ',1-(1-p_data**2)**3,f'  soit {(1-(1-p_data**2)**3)*100:.2f}%')\n",
+    "print()\n",
+    "print(\"data2  p         : \",p_data2)\n",
+    "print('data2  p^2       : ',p_data2**2)\n",
+    "print('data2  1−(1−𝑝2)3 : ',1-(1-p_data2**2)**3,f'  soit {(1-(1-p_data2**2)**3)*100:.2f}%')\n",
+    "print (\"\")"
   ]
  },
  {
@@ -705,7 +1293,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.4"
  }
 },
 "nbformat": 4,

--- a/module3/exo1/analyse-syndrome-grippal.ipynb
+++ b/module3/exo1/analyse-syndrome-grippal.ipynb
--- a/module3/exo1/mod3_exo1_donnes_brutes.csv
+++ b/module3/exo1/mod3_exo1_donnes_brutes.csv