{"id":3438,"date":"2023-07-17T11:29:47","date_gmt":"2023-07-17T11:29:47","guid":{"rendered":"https:\/\/statorials.org\/uk\/statsmodels-%d0%bb%d1%96%d0%bd%d1%96%d0%b8%d0%bd%d0%b0-%d1%80%d0%b5%d0%b3%d1%80%d0%b5%d1%81%d1%96%d1%8f-p-%d0%b7%d0%bd%d0%b0%d1%87%d0%b5%d0%bd%d0%bd%d1%8f\/"},"modified":"2023-07-17T11:29:47","modified_gmt":"2023-07-17T11:29:47","slug":"statsmodels-%d0%bb%d1%96%d0%bd%d1%96%d0%b8%d0%bd%d0%b0-%d1%80%d0%b5%d0%b3%d1%80%d0%b5%d1%81%d1%96%d1%8f-p-%d0%b7%d0%bd%d0%b0%d1%87%d0%b5%d0%bd%d0%bd%d1%8f","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/statsmodels-%d0%bb%d1%96%d0%bd%d1%96%d0%b8%d0%bd%d0%b0-%d1%80%d0%b5%d0%b3%d1%80%d0%b5%d1%81%d1%96%d1%8f-p-%d0%b7%d0%bd%d0%b0%d1%87%d0%b5%d0%bd%d0%bd%d1%8f\/","title":{"rendered":"\u042f\u043a \u043e\u0442\u0440\u0438\u043c\u0430\u0442\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f p \u0456\u0437 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043d\u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u044f\u0445"},"content":{"rendered":"

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 #extract p-values for all predictor variables\n<\/span>for<\/span> x in<\/span> range(0, 3):\n    print<\/span> ( model.pvalues<\/span> [x])\n\n#extract p-value for specific predictor variable name\n<\/span>model. pvalues<\/span> . loc<\/span> [' predictor1<\/span> ']\n\n#extract p-value for specific predictor variable position<\/span>\nmodel. pvalues<\/span> [0]\n<\/strong><\/pre>\n
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 \u041f\u0440\u0438\u043a\u043b\u0430\u0434: \u0432\u0438\u0442\u044f\u0433\u043d\u0456\u0442\u044c P-\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0437 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432 \u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u043d\u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u044f\u0445<\/strong><\/span><\/h2>\n
 \u041f\u0440\u0438\u043f\u0443\u0441\u0442\u0456\u043c\u043e, \u0449\u043e \u0443 \u043d\u0430\u0441 \u0454 \u043d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 pandas DataFrame, \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0456\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0456\u044e \u043f\u0440\u043e \u0432\u0438\u0432\u0447\u0435\u043d\u0456 \u0433\u043e\u0434\u0438\u043d\u0438, \u0441\u043a\u043b\u0430\u0434\u0435\u043d\u0456 \u043f\u0456\u0434\u0433\u043e\u0442\u043e\u0432\u0447\u0456 \u0456\u0441\u043f\u0438\u0442\u0438 \u0442\u0430 \u043f\u0456\u0434\u0441\u0443\u043c\u043a\u043e\u0432\u0443 \u043e\u0446\u0456\u043d\u043a\u0443, \u043e\u0442\u0440\u0438\u043c\u0430\u043d\u0443 \u0443\u0447\u043d\u044f\u043c\u0438 \u0432 \u043f\u0435\u0432\u043d\u043e\u043c\u0443 \u043a\u043b\u0430\u0441\u0456:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4, 3, 6],\n                   ' exams<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4, 3, 2],\n                   ' score<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90, 75, 96]})\n\n#view head of DataFrame\n<\/span>df. head<\/span> ()\n\n\thours exam score\n0 1 1 76\n1 2 3 78\n2 2 3 85\n3 4 5 88\n4 2 2 72<\/strong><\/pre>\n
 import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define predictor and response variables\n<\/span>y = df['score']\nx = df[['hours', 'exams']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#fit linear regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view model summary\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: R-squared score: 0.718\nModel: OLS Adj. R-squared: 0.661\nMethod: Least Squares F-statistic: 12.70\nDate: Fri, 05 Aug 2022 Prob (F-statistic): 0.00180\nTime: 09:24:38 Log-Likelihood: -38.618\nNo. Observations: 13 AIC: 83.24\nDf Residuals: 10 BIC: 84.93\nDf Model: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 71.4048 4.001 17.847 0.000 62.490 80.319\nhours 5.1275 1.018 5.038 0.001 2.860 7.395\nexams -1.2121 1.147 -1.057 0.315 -3.768 1.344\n==================================================== ============================\nOmnibus: 1,103 Durbin-Watson: 1,248\nProb(Omnibus): 0.576 Jarque-Bera (JB): 0.803\nSkew: -0.289 Prob(JB): 0.669\nKurtosis: 1.928 Cond. No. 11.7\n==================================================== ============================\n<\/strong><\/pre>\n
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