{"id":3441,"date":"2023-07-17T11:29:47","date_gmt":"2023-07-17T11:29:47","guid":{"rendered":"https:\/\/statorials.org\/tr\/statsmodels-dogrusal-regresyon-p-degeri\/"},"modified":"2023-07-17T11:29:47","modified_gmt":"2023-07-17T11:29:47","slug":"statsmodels-dogrusal-regresyon-p-degeri","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/statsmodels-dogrusal-regresyon-p-degeri\/","title":{"rendered":"I\u0307statistiksel modellerde do\u011frusal regresyondan p de\u011ferleri nas\u0131l \u00e7\u0131kar\u0131l\u0131r?"},"content":{"rendered":"

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Python’daki statsmodels<\/a> mod\u00fcl\u00fcn\u00fc kullanarak do\u011frusal regresyon modeline uygun katsay\u0131lar i\u00e7in p de\u011ferlerini \u00e7\u0131karmak i\u00e7in a\u015fa\u011f\u0131daki y\u00f6ntemleri kullanabilirsiniz:<\/span><\/p>\n

 #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
 A\u015fa\u011f\u0131daki \u00f6rnekler her y\u00f6ntemin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6stermektedir.<\/span><\/p>\n
 \u00d6rnek: \u0130statistiksel modellerde do\u011frusal regresyondan P de\u011ferlerini \u00e7\u0131kar\u0131n<\/strong><\/span><\/h2>\n
 Belirli bir s\u0131n\u0131ftaki \u00f6\u011frencilerin \u00e7al\u0131\u015f\u0131lan saatler, girilen haz\u0131rl\u0131k s\u0131navlar\u0131 ve ald\u0131klar\u0131 final notlar\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/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
 Varsay\u0131lan olarak, Summary()<\/strong> i\u015flevi, her \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkenin p de\u011ferlerini \u00fc\u00e7 ondal\u0131k basama\u011fa kadar g\u00f6r\u00fcnt\u00fcler:<\/span><\/p>\n
 Tahmin edici de\u011fi\u015fkenler olarak “saatler” ve “s\u0131navlar”\u0131 ve yan\u0131t de\u011fi\u015fkeni<\/a> olarak “puan”\u0131 kullanarak \u00e7oklu do\u011frusal regresyon modeline<\/a> uyum sa\u011flamak i\u00e7in statsmodels mod\u00fcl\u00fcn\u00fcn OLS()<\/strong> i\u015flevini kullanabiliriz:<\/span><\/p>\n