diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 26ad6d94fa840f788a57621b06dc6af83a848391..ceef0b8ef5c8edf6b9e89640e76b088af6254f42 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -261,33 +261,33 @@ "" ], "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" + " 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": 1, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -355,6 +355,14 @@ " \n", " \n", " \n", + " 0\n", + " 4/12/81\n", + " 6\n", + " 66\n", + " 50\n", + " 0\n", + " \n", + " \n", " 1\n", " 11/12/81\n", " 6\n", @@ -363,6 +371,54 @@ " 1\n", " \n", " \n", + " 2\n", + " 3/22/82\n", + " 6\n", + " 69\n", + " 50\n", + " 0\n", + " \n", + " \n", + " 3\n", + " 11/11/82\n", + " 6\n", + " 68\n", + " 50\n", + " 0\n", + " \n", + " \n", + " 4\n", + " 4/04/83\n", + " 6\n", + " 67\n", + " 50\n", + " 0\n", + " \n", + " \n", + " 5\n", + " 6/18/82\n", + " 6\n", + " 72\n", + " 50\n", + " 0\n", + " \n", + " \n", + " 6\n", + " 8/30/83\n", + " 6\n", + " 73\n", + " 100\n", + " 0\n", + " \n", + " \n", + " 7\n", + " 11/28/83\n", + " 6\n", + " 70\n", + " 100\n", + " 0\n", + " \n", + " \n", " 8\n", " 2/03/84\n", " 6\n", @@ -387,6 +443,22 @@ " 1\n", " \n", " \n", + " 11\n", + " 10/05/84\n", + " 6\n", + " 78\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 12\n", + " 11/08/84\n", + " 6\n", + " 67\n", + " 200\n", + " 0\n", + " \n", + " \n", " 13\n", " 1/24/85\n", " 6\n", @@ -395,6 +467,54 @@ " 2\n", " \n", " \n", + " 14\n", + " 4/12/85\n", + " 6\n", + " 67\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 15\n", + " 4/29/85\n", + " 6\n", + " 75\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 16\n", + " 6/17/85\n", + " 6\n", + " 70\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 17\n", + " 7/29/85\n", + " 6\n", + " 81\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 18\n", + " 8/27/85\n", + " 6\n", + " 76\n", + " 200\n", + " 0\n", + " \n", + " \n", + " 19\n", + " 10/03/85\n", + " 6\n", + " 79\n", + " 200\n", + " 0\n", + " \n", + " \n", " 20\n", " 10/30/85\n", " 6\n", @@ -403,6 +523,14 @@ " 2\n", " \n", " \n", + " 21\n", + " 11/26/85\n", + " 6\n", + " 76\n", + " 200\n", + " 0\n", + " \n", + " \n", " 22\n", " 1/12/86\n", " 6\n", @@ -416,22 +544,38 @@ ], "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": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data = data[data.Malfunction>0]\n", + "#data = data[data.Malfunction>0]\n", "data" ] }, @@ -448,12 +592,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -474,6 +618,76 @@ "plt.grid(True)" ] }, + { + "cell_type": "code", + "execution_count": 5, + "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=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Test du Chi-deux : Chi2 = 317.08509556162534, p-valeur = 4.1270772491122487e-54\n" + ] + } + ], + "source": [ + "#(in)dépendance entre température et pression\n", + "from scipy.stats import chi2_contingency\n", + "# Créez un tableau de contingence à partir de vos données\n", + "contingency_table = np.array([data[\"Temperature\"], data[\"Pressure\"]]).T\n", + "\n", + "# Effectuez le test du chi-deux\n", + "chi2, p, _, _ = chi2_contingency(contingency_table)\n", + "\n", + "# Affichez les résultats\n", + "print(f\"Test du Chi-deux : Chi2 = {chi2}, p-valeur = {p}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false + }, + "source": [ + "Les variables températures et pression sont dépendantes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -500,7 +714,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -509,10 +723,10 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -521,16 +735,16 @@ " \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: 7Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 5Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -2.5250Method: IRLS Log-Likelihood: -3.8368
Date: Sat, 13 Apr 2019 Deviance: 0.22231Date: Wed, 06 Dec 2023 Deviance: 2.8461
Time: 19:11:24 Pearson chi2: 0.236Time: 09:39:06 Pearson chi2: 4.18
No. Iterations: 4 Covariance Type: nonrobustNo. Iterations: 6 Covariance Type: nonrobust
\n", "\n", @@ -538,10 +752,10 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953Pressure 0.0119 0.018 0.678 0.498 -0.023 0.046
Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240Temperature -0.0697 0.050 -1.381 0.167 -0.169 0.029
" ], @@ -550,24 +764,24 @@ "\"\"\"\n", " Generalized Linear Model Regression Results \n", "==============================================================================\n", - "Dep. Variable: Frequency No. Observations: 7\n", - "Model: GLM Df Residuals: 5\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: -2.5250\n", - "Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", - "Time: 19:11:24 Pearson chi2: 0.236\n", - "No. Iterations: 4 Covariance Type: nonrobust\n", + "Method: IRLS Log-Likelihood: -3.8368\n", + "Date: Wed, 06 Dec 2023 Deviance: 2.8461\n", + "Time: 09:39:06 Pearson chi2: 4.18\n", + "No. Iterations: 6 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", - "Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n", + "Pressure 0.0119 0.018 0.678 0.498 -0.023 0.046\n", + "Temperature -0.0697 0.050 -1.381 0.167 -0.169 0.029\n", "===============================================================================\n", "\"\"\"" ] }, - "execution_count": 4, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -578,7 +792,7 @@ "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", + "logmodel=sm.GLM(data['Frequency'], data[['Pressure','Temperature']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n", "\n", "logmodel.summary()" ] @@ -605,12 +819,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -705,7 +919,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4,