{"id":3442,"date":"2023-07-17T11:29:47","date_gmt":"2023-07-17T11:29:47","guid":{"rendered":"https:\/\/statorials.org\/cn\/statsmodels-%e7%ba%bf%e6%80%a7%e5%9b%9e%e5%bd%92-p-%e5%80%bc\/"},"modified":"2023-07-17T11:29:47","modified_gmt":"2023-07-17T11:29:47","slug":"statsmodels-%e7%ba%bf%e6%80%a7%e5%9b%9e%e5%bd%92-p-%e5%80%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/statsmodels-%e7%ba%bf%e6%80%a7%e5%9b%9e%e5%bd%92-p-%e5%80%bc\/","title":{"rendered":"\u5982\u4f55\u4ece\u7edf\u8ba1\u6a21\u578b\u4e2d\u7684\u7ebf\u6027\u56de\u5f52\u4e2d\u63d0\u53d6p\u503c"},"content":{"rendered":"

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\u60a8\u53ef\u4ee5\u4f7f\u7528Python\u4e2d\u7684statsmodels<\/a>\u6a21\u5757\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\u63d0\u53d6\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u62df\u5408\u4e2d\u7cfb\u6570\u7684p\u503c\uff1a<\/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
\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u6bcf\u79cd\u65b9\u6cd5\u3002<\/span><\/p>\n
\u793a\u4f8b\uff1a\u4ece\u7edf\u8ba1\u6a21\u578b\u4e2d\u7684\u7ebf\u6027\u56de\u5f52\u4e2d\u63d0\u53d6 P \u503c<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff0c\u5176\u4e2d\u5305\u542b\u6709\u5173\u67d0\u4e2a\u73ed\u7ea7\u5b66\u751f\u7684\u5b66\u4e60\u65f6\u95f4\u3001\u51c6\u5907\u8003\u8bd5\u4ee5\u53ca\u6700\u7ec8\u6210\u7ee9\u7684\u4fe1\u606f\uff1a<\/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
\u9ed8\u8ba4\u60c5\u51b5\u4e0b\uff0c summary()<\/strong>\u51fd\u6570\u663e\u793a\u6bcf\u4e2a\u9884\u6d4b\u53d8\u91cf\u7684 p \u503c\uff0c\u6700\u591a\u663e\u793a\u5c0f\u6570\u70b9\u540e\u4e09\u4f4d\uff1a<\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 statsmodels \u6a21\u5757\u7684OLS()<\/strong>\u51fd\u6570\u6765\u62df\u5408\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\uff0c\u4f7f\u7528\u201c\u5c0f\u65f6\u201d\u548c\u201c\u8003\u8bd5\u201d\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c\u201c\u5206\u6570\u201d\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf<\/a>\uff1a<\/span><\/p>\n