{"id":3439,"date":"2023-07-17T11:29:47","date_gmt":"2023-07-17T11:29:47","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%90%e1%85%a9%e1%86%bc%e1%84%80%e1%85%a8%e1%84%86%e1%85%a9%e1%84%83%e1%85%a6%e1%86%af-%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-p-%e1%84%80\/"},"modified":"2023-07-17T11:29:47","modified_gmt":"2023-07-17T11:29:47","slug":"%e1%84%90%e1%85%a9%e1%86%bc%e1%84%80%e1%85%a8%e1%84%86%e1%85%a9%e1%84%83%e1%85%a6%e1%86%af-%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-p-%e1%84%80","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%90%e1%85%a9%e1%86%bc%e1%84%80%e1%85%a8%e1%84%86%e1%85%a9%e1%84%83%e1%85%a6%e1%86%af-%e1%84%89%e1%85%a5%e1%86%ab%e1%84%92%e1%85%a7%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-p-%e1%84%80\/","title":{"rendered":"\ud1b5\uacc4 \ubaa8\ub378\uc758 \uc120\ud615 \ud68c\uadc0\uc5d0\uc11c p \uac12\uc744 \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

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Python\uc758 statsmodels<\/a> \ubaa8\ub4c8\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378 \ud53c\ud305\uc5d0\uc11c \uacc4\uc218\uc5d0 \ub300\ud55c p-\uac12\uc744 \ucd94\ucd9c\ud558\ub824\uba74 \ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uac01 \ubc29\ubc95\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: \ud1b5\uacc4 \ubaa8\ub378\uc758 \uc120\ud615 \ud68c\uadc0\uc5d0\uc11c P \uac12 \ucd94\ucd9c<\/strong><\/span><\/h2>\n
 \ud559\uc2b5 \uc2dc\uac04, \uc751\uc2dc\ud55c \uc900\ube44 \uc2dc\ud5d8, \ud2b9\uc815 \uc218\uc5c5\uc758 \ud559\uc0dd\ub4e4\uc774 \ubc1b\uc740 \ucd5c\uc885 \uc131\uc801\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c\uacfc \uac19\uc740 pandas DataFrame\uc774 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/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
 \uae30\ubcf8\uc801\uc73c\ub85c summary()<\/strong> \ud568\uc218\ub294 \uac01 \uc608\uce21 \ubcc0\uc218\uc758 p-\uac12\uc744 \uc18c\uc218\uc810 \uc774\ud558 \uc138 \uc790\ub9ac\uae4c\uc9c0 \ud45c\uc2dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 statsmodels \ubaa8\ub4c8\uc758 OLS()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec “\uc2dc\uac04”\uacfc “\uc2dc\ud5d8”\uc744 \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 “\uc810\uc218” \ub97c \uc751\ub2f5 \ubcc0\uc218<\/a> \ub85c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378<\/a> \uc5d0 \ub9de\ucd9c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n