{"id":3568,"date":"2023-07-16T18:41:15","date_gmt":"2023-07-16T18:41:15","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e7%b5%b1%e8%a8%88%e3%83%a2%e3%83%86%e3%82%99%e3%83%ab%e3%81%8b%e3%82%99%e4%ba%88%e6%b8%ac%e3%81%99%e3%82%8b\/"},"modified":"2023-07-16T18:41:15","modified_gmt":"2023-07-16T18:41:15","slug":"%e7%b5%b1%e8%a8%88%e3%83%a2%e3%83%86%e3%82%99%e3%83%ab%e3%81%8b%e3%82%99%e4%ba%88%e6%b8%ac%e3%81%99%e3%82%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e7%b5%b1%e8%a8%88%e3%83%a2%e3%83%86%e3%82%99%e3%83%ab%e3%81%8b%e3%82%99%e4%ba%88%e6%b8%ac%e3%81%99%e3%82%8b\/","title":{"rendered":"Statsmodels \u3067\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u4e88\u6e2c\u3092\u884c\u3046\u65b9\u6cd5"},"content":{"rendered":"

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\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001Python \u306estatsmodels<\/a>\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u4f7f\u7528\u3057\u305f\u56de\u5e30\u30e2\u30c7\u30eb \u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u3092\u4f7f\u7528\u3057\u3066\u3001\u65b0\u3057\u3044\u89b3\u6e2c\u5024\u306b\u3064\u3044\u3066\u306e\u4e88\u6e2c\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n

 model. predict<\/span> (df_new)\n<\/strong><\/pre>\n
\u3053\u306e\u7279\u5b9a\u306e\u69cb\u6587\u306f\u3001model \u3068\u547c\u3070\u308c\u308b\u7d71\u8a08\u30e2\u30c7\u30eb<\/strong>\u306b\u9069\u3057\u305f\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001 df_new<\/strong>\u3068\u547c\u3070\u308c\u308b\u65b0\u3057\u3044 DataFrame \u306e\u5404\u884c\u306e\u4e88\u6e2c\u5fdc\u7b54\u5024\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u4f8b: Statsmodels \u306e\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u305f\u4e88\u6e2c\u306e\u4f5c\u6210<\/strong><\/span><\/h2>\n
\u7279\u5b9a\u306e\u30af\u30e9\u30b9\u306e\u751f\u5f92\u306e\u5b66\u7fd2\u6642\u9593\u3001\u53d7\u3051\u305f\u4e88\u5099\u8a66\u9a13\u3001\u304a\u3088\u3073\u6700\u7d42\u6210\u7e3e\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/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<\/span> ']\nx = df[[' hours<\/span> ', ' exams<\/span> ']]\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
\u51fa\u529b\u306ecoef<\/strong>\u5217\u304b\u3089\u3001\u8fd1\u4f3c\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u30b9\u30b3\u30a2 = 71.4048 + 5.1275 (\u6642\u9593) \u2013 1.2121 (\u8a66\u9a13)<\/span><\/p>\n
\u3053\u3053\u3067\u3001\u9069\u5408\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066 5 \u4eba\u306e\u65b0\u5165\u751f\u306e\u300c\u5f97\u70b9\u300d\u3092\u4e88\u6e2c\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u307e\u305a\u30015 \u3064\u306e\u65b0\u3057\u3044\u89b3\u6e2c\u5024\u3092\u4fdd\u6301\u3059\u308b DataFrame \u3092\u4f5c\u6210\u3057\u307e\u3057\u3087\u3046\u3002<\/span><\/p>\n
 #create new DataFrame\ndf_new = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 2, 4, 5],\n                       ' exams<\/span> ': [1, 1, 4, 3, 3]})<\/span>\n\n#add column for constant\ndf_new = sm. add_constant<\/span> (df_new)\n<\/span>\n#view new DataFrame\nprint<\/span> (df_new)\n\n   const hours exams\n0 1.0 1 1\n1 1.0 2 1\n2 1.0 2 4\n3 1.0 4 3\n4 1.0 5 3<\/span><\/span><\/strong><\/pre>\n
\u6b21\u306b\u3001 predict()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u9069\u5408\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u5909\u6570\u306e\u5024\u3068\u3057\u3066\u300c\u6642\u9593\u6570\u300d\u3068\u300c\u8a66\u9a13\u300d\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u308c\u3089\u306e\u5404\u751f\u5f92\u306e\u300c\u30b9\u30b3\u30a2\u300d\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/span><\/p>\n
 #predict scores for the five new students<\/span>\nmodel. predict<\/span> (df_new)\n\n0 75.320242\n1 80.447734\n2 76.811480\n3 88.278550\n4 93.406042\ndtype:float64\n<\/strong><\/pre>\n
\u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n
 statsmodels \u30e2\u30b8\u30e5\u30fc\u30eb\u306eOLS()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u300c\u6642\u9593\u300d\u3068\u300c\u8a66\u9a13\u300d\u3092\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001\u300c\u30b9\u30b3\u30a2\u300d\u3092\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u3092\u8fd1\u4f3c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n