{"id":2116,"date":"2023-07-23T15:01:59","date_gmt":"2023-07-23T15:01:59","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e5%ae%8c%e7%92%a7%e3%81%aa%e5%a4%9a%e9%87%8d%e5%85%b1%e7%b7%9a%e6%80%a7\/"},"modified":"2023-07-23T15:01:59","modified_gmt":"2023-07-23T15:01:59","slug":"%e5%ae%8c%e7%92%a7%e3%81%aa%e5%a4%9a%e9%87%8d%e5%85%b1%e7%b7%9a%e6%80%a7","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e5%ae%8c%e7%92%a7%e3%81%aa%e5%a4%9a%e9%87%8d%e5%85%b1%e7%b7%9a%e6%80%a7\/","title":{"rendered":"\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u3068\u306f\u4f55\u3067\u3059\u304b? (\u5b9a\u7fa9\u3068\u4f8b)"},"content":{"rendered":"

<\/p>\n

\n

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\u5909\u6570\u9593\u306e\u76f8\u95a2\u5ea6\u304c\u5341\u5206\u306b\u9ad8\u3044\u5834\u5408\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u3068\u89e3\u91c8\u306e\u969b\u306b\u554f\u984c\u304c\u767a\u751f\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u591a\u91cd\u5171\u7dda\u6027\u306e\u6700\u3082\u6975\u7aef\u306a\u30b1\u30fc\u30b9\u306f\u3001\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027<\/strong>\u3068\u547c\u3070\u308c\u307e\u3059\u3002\u3053\u308c\u306f\u30012 \u3064\u4ee5\u4e0a\u306e\u4e88\u6e2c\u5909\u6570\u304c\u4e92\u3044\u306b\u6b63\u78ba\u306a\u7dda\u5f62\u95a2\u4fc2\u3092\u6301\u3064\u5834\u5408\u306b\u767a\u751f\u3057\u307e\u3059\u3002<\/span><\/p>\n

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\"\"<\/p>\n

\u4e88\u6e2c\u5b50\u5909\u6570 x 2<\/sub>\u306e\u5024\u306f\u3001\u5358\u306b x 1<\/sub>\u306e\u5024\u3092 2 \u500d\u3057\u305f\u3082\u306e\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span> <\/p>\n

\"\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u306e\u4f8b\"<\/p>\n

\u3053\u308c\u306f\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027<\/strong>\u306e\u4f8b\u3067\u3059\u3002<\/span><\/p>\n

\u5b8c\u5168\u591a\u91cd\u5171\u7dda\u6027\u306e\u554f\u984c<\/strong><\/span><\/h2>\n

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\u5b9f\u969b\u3001\u5225\u306e\u4e88\u6e2c\u5b50\u5909\u6570 (x 2<\/sub> ) \u3092\u4e00\u5b9a\u306b\u4fdd\u3061\u306a\u304c\u3089\u3001\u5fdc\u7b54\u5909\u6570 (y) \u306b\u5bfe\u3059\u308b\u4e88\u6e2c\u5b50\u5909\u6570 (x 1<\/sub> ) \u306e\u9650\u754c\u52b9\u679c\u3092\u63a8\u5b9a\u3059\u308b\u3053\u3068\u306f\u4e0d\u53ef\u80fd\u3067\u3059\u3002\u3053\u308c\u306f\u3001x 1 \u304c\u79fb\u52d5\u3059\u308b\u3068\u5e38\u306b\u6b63\u78ba\u306b x 2<\/sub>\u304c<\/sub>\u79fb\u52d5\u3059\u308b\u305f\u3081\u3067\u3059\u3002<\/span><\/p>\n

\u3064\u307e\u308a\u3001\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u306b\u3088\u308a\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u5404\u4fc2\u6570\u306e\u5024\u3092\u63a8\u5b9a\u3059\u308b\u3053\u3068\u304c\u4e0d\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u306b\u5bfe\u51e6\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h2>\n

\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u3092\u51e6\u7406\u3059\u308b\u6700\u3082\u7c21\u5358\u306a\u65b9\u6cd5\u306f\u3001\u5225\u306e\u5909\u6570\u3068\u6b63\u78ba\u306a\u7dda\u5f62\u95a2\u4fc2\u304c\u3042\u308b\u5909\u6570\u306e 1 \u3064\u3092\u524a\u9664\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n

\u305f\u3068\u3048\u3070\u3001\u524d\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u306f\u3001\u5358\u7d14\u306b x 2 \u3092<\/sub>\u4e88\u6e2c\u5b50\u5909\u6570\u3068\u3057\u3066\u524a\u9664\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n

\"\"<\/p>\n

\u6b21\u306b\u3001x 1<\/sub>\u3092\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001y \u3092\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u306e\u4f8b<\/strong><\/span><\/h2>\n

\u6b21\u306e\u4f8b\u306f\u3001\u5b9f\u969b\u306b\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u3092\u793a\u3059 3 \u3064\u306e\u6700\u3082\u4e00\u822c\u7684\u306a\u30b7\u30ca\u30ea\u30aa\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n

1. \u4e88\u6e2c\u5909\u6570\u306f\u5225\u306e\u5909\u6570\u306e\u500d\u6570\u3067\u3059<\/strong><\/span><\/h3>\n

\u300c\u8eab\u9577\uff08\u30bb\u30f3\u30c1\u30e1\u30fc\u30c8\u30eb\uff09\u300d\u3068\u300c\u8eab\u9577\uff08\u30e1\u30fc\u30c8\u30eb\uff09\u300d\u3092\u4f7f\u7528\u3057\u3066\u3001\u7279\u5b9a\u306e\u7a2e\u985e\u306e\u30a4\u30eb\u30ab\u306e\u4f53\u91cd\u3092\u4e88\u6e2c\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/span> <\/p>\n

\"\"<\/p>\n

\u300c\u30bb\u30f3\u30c1\u30e1\u30fc\u30c8\u30eb\u5358\u4f4d\u306e\u9ad8\u3055\u300d\u306e\u5024\u306f\u3001\u5358\u306b\u300c\u30e1\u30fc\u30c8\u30eb\u5358\u4f4d\u306e\u9ad8\u3055\u300d\u3092 100 \u500d\u3057\u305f\u3082\u306e\u306b\u7b49\u3057\u3044\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3053\u308c\u306f\u3001\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u306e\u30b1\u30fc\u30b9\u3067\u3059\u3002<\/span><\/p>\n

\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066 R \u3067\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u3088\u3046\u3068\u3059\u308b\u3068\u3001\u4e88\u6e2c\u5909\u6570\u300c\u30e1\u30fc\u30c8\u30eb\u300d\u306e\u4fc2\u6570\u63a8\u5b9a\u5024\u3092\u751f\u6210\u3067\u304d\u307e\u305b\u3093\u3002<\/span><\/p>\n

 #define data\n<\/span>df <- data. frame<\/span> (weight=c(400, 460, 470, 475, 490, 440, 430, 490, 500, 540),\n                 m=c(1.3, .7, .6, 1.3, 1.2, 1.5, 1.2, 1.6, 1.1, 1.4),\n                 cm=c(130, 70, 60, 130, 120, 150, 120, 160, 110, 140))\n\n#fit multiple linear regression model\n<\/span>model <- lm(weight~m+cm, data=df)\n\n#view summary of model\n<\/span>summary(model)\n\nCall:\nlm(formula = weight ~ m + cm, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-70,501 -25,501 5,183 19,499 68,590 \n\nCoefficients: (1 not defined because of singularities)\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 458,676 53,403 8,589 2.61e-05 ***\nm 9.096 43.473 0.209 0.839    \ncm NA NA NA NA    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 41.9 on 8 degrees of freedom\nMultiple R-squared: 0.005442, Adjusted R-squared: -0.1189 \nF-statistic: 0.04378 on 1 and 8 DF, p-value: 0.8395<\/strong><\/pre>\n
 2. \u4e88\u6e2c\u5909\u6570\u306f\u5225\u306e\u5909\u6570\u306e\u5909\u63db\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3059<\/strong><\/span><\/h3>\n
\u300c\u30dd\u30a4\u30f3\u30c8\u300d\u3068\u300c\u30b9\u30b1\u30fc\u30eb \u30dd\u30a4\u30f3\u30c8\u300d\u3092\u4f7f\u7528\u3057\u3066\u30d0\u30b9\u30b1\u30c3\u30c8\u30dc\u30fc\u30eb\u9078\u624b\u306e\u8a55\u4fa1\u3092\u4e88\u6e2c\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u5909\u6570\u300c\u30b9\u30b1\u30fc\u30eb\u3055\u308c\u305f\u30dd\u30a4\u30f3\u30c8\u300d\u304c\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u30b9\u30b1\u30fc\u30eb\u3055\u308c\u305f\u30dd\u30a4\u30f3\u30c8 = (\u30dd\u30a4\u30f3\u30c8 \u2013 \u03bc\u30dd\u30a4\u30f3\u30c8<\/sub>) \/ \u03c3\u30dd\u30a4\u30f3\u30c8<\/sub><\/span><\/p>\n
\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/span> <\/p>\n
\"\"<\/p>\n
\u5404\u300c\u30b9\u30b1\u30fc\u30eb\u3055\u308c\u305f\u30dd\u30a4\u30f3\u30c8\u300d\u306e\u5024\u306f\u3001\u5358\u306b\u300c\u30dd\u30a4\u30f3\u30c8\u300d\u306e\u6a19\u6e96\u5316\u3055\u308c\u305f\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3042\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u3053\u308c\u306f\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u306e\u30b1\u30fc\u30b9\u3067\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066 R \u3067\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u3088\u3046\u3068\u3059\u308b\u3068\u3001\u300c\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3055\u308c\u305f\u30dd\u30a4\u30f3\u30c8\u300d\u4e88\u6e2c\u5909\u6570\u306e\u4fc2\u6570\u63a8\u5b9a\u5024\u3092\u751f\u6210\u3067\u304d\u307e\u305b\u3093\u3002<\/span><\/p>\n
 #define data\n<\/span>df <- data. frame<\/span> (rating=c(88, 83, 90, 94, 96, 78, 79, 91, 90, 82),\n                 pts=c(17, 19, 24, 29, 33, 15, 14, 29, 25, 22))\n\ndf$scaled_pts <- (df$pts - mean(df$pts)) \/ sd(df$pts)\n\n#fit multiple linear regression model\n<\/span>model <- lm(rating~pts+scaled_pts, data=df)\n\n#view summary of model\n<\/span>summary(model)\n\nCall:\nlm(formula = rating ~ pts + scaled_pts, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-4.4932 -1.3941 -0.2935 1.3055 5.8412 \n\nCoefficients: (1 not defined because of singularities)\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 67.4218 3.5896 18.783 6.67e-08 ***\npts 0.8669 0.1527 5.678 0.000466 ***\nscaled_pts NA NA NA NA    \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.953 on 8 degrees of freedom\nMultiple R-squared: 0.8012, Adjusted R-squared: 0.7763 \nF-statistic: 32.23 on 1 and 8 DF, p-value: 0.0004663\n<\/strong><\/pre>\n
 3. \u30c0\u30df\u30fc\u5909\u6570\u30c8\u30e9\u30c3\u30d7<\/strong><\/span><\/h3>\n
\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u304c\u767a\u751f\u3059\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u5225\u306e\u30b7\u30ca\u30ea\u30aa\u306f\u3001 \u30c0\u30df\u30fc\u5909\u6570\u30c8\u30e9\u30c3\u30d7<\/a>\u3068\u3057\u3066\u77e5\u3089\u308c\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u30ab\u30c6\u30b4\u30ea\u5909\u6570\u3092\u53d6\u5f97\u3057\u3001\u305d\u308c\u3092 0\u30011\u30012 \u306a\u3069\u306e\u5024\u3092\u53d6\u308b\u300c\u30c0\u30df\u30fc\u5909\u6570\u300d\u306b\u5909\u63db\u3057\u305f\u3044\u5834\u5408\u3067\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u3001\u4e88\u6e2c\u5909\u6570\u300c\u5e74\u9f62\u300d\u3068\u300c\u5a5a\u59fb\u72b6\u6cc1\u300d\u3092\u4f7f\u7528\u3057\u3066\u53ce\u5165\u3092\u4e88\u6e2c\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span> <\/p>\n
\"\"<\/p>\n
 \u300c\u5a5a\u59fb\u72b6\u6cc1\u300d\u3092\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3059\u308b\u306b\u306f\u3001\u307e\u305a\u305d\u308c\u3092\u30c0\u30df\u30fc\u5909\u6570\u306b\u5909\u63db\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u308c\u3092\u884c\u3046\u306b\u306f\u3001\u3053\u308c\u304c\u6700\u3082\u983b\u7e41\u306b\u767a\u751f\u3059\u308b\u305f\u3081\u3001\u300cSingle\u300d\u3092\u57fa\u672c\u5024\u3068\u3057\u3066\u6b8b\u3057\u3001\u6b21\u306e\u3088\u3046\u306b\u300cMarried\u300d\u3068\u300cDivorce\u300d\u306b 0 \u307e\u305f\u306f 1 \u306e\u5024\u3092\u5272\u308a\u5f53\u3066\u307e\u3059\u3002<\/span> <\/p>\n
\"\"<\/p>\n
\u9593\u9055\u3044\u306f\u3001\u6b21\u306e\u3088\u3046\u306b 3 \u3064\u306e\u65b0\u3057\u3044\u30c0\u30df\u30fc\u5909\u6570\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span> <\/p>\n
\"\"<\/p>\n
\u3053\u306e\u5834\u5408\u3001\u300c\u72ec\u8eab\u300d\u5909\u6570\u306f\u300c\u65e2\u5a5a\u300d\u5909\u6570\u3068\u300c\u96e2\u5a5a\u300d\u5909\u6570\u306e\u5b8c\u5168\u306a\u7dda\u5f62\u7d50\u5408\u3067\u3059\u3002\u3053\u308c\u306f\u5b8c\u5168\u306a\u591a\u91cd\u5171\u7dda\u6027\u306e\u4f8b\u3067\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066 R \u3067\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u3088\u3046\u3068\u3059\u308b\u3068\u3001\u5404\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u4fc2\u6570\u63a8\u5b9a\u5024\u3092\u751f\u6210\u3067\u304d\u306a\u304f\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n
 #define data\n<\/span>df <- data. frame<\/span> (income=c(45, 48, 54, 57, 65, 69, 78, 83, 98, 104, 107),\n                 age=c(23, 25, 24, 29, 38, 36, 40, 59, 56, 64, 53),\n                 single=c(1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0),\n                 married=c(0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1),\n                 divorced=c(0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0))\n\n#fit multiple linear regression model\n<\/span>model <- lm(income~age+single+married+divorced, data=df)\n\n#view summary of model\n<\/span>summary(model)\n\nCall:\nlm(formula = income ~ age + single + married + divorced, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-9.7075 -5.0338 0.0453 3.3904 12.2454 \n\nCoefficients: (1 not defined because of singularities)\n            Estimate Std. Error t value Pr(>|t|)   \n(Intercept) 16.7559 17.7811 0.942 0.37739   \nage 1.4717 0.3544 4.152 0.00428 **\nsingle -2.4797 9.4313 -0.263 0.80018   \nmarried NA NA NA NA   \ndivorced -8.3974 12.7714 -0.658 0.53187   \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 8.391 on 7 degrees of freedom\nMultiple R-squared: 0.9008, Adjusted R-squared: 0.8584 \nF-statistic: 21.2 on 3 and 7 DF, p-value: 0.0006865\n<\/strong><\/pre>\n
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
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R\u3067VIF\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a>
Python \u3067 VIF \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a>
Excel\u3067VIF\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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