{"id":1404,"date":"2023-07-26T12:37:50","date_gmt":"2023-07-26T12:37:50","guid":{"rendered":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e3%82%bf%e3%82%99%e3%83%9f%e3%83%bc%e5%a4%89%e6%95%b0\/"},"modified":"2023-07-26T12:37:50","modified_gmt":"2023-07-26T12:37:50","slug":"r%e3%81%ae%e3%82%bf%e3%82%99%e3%83%9f%e3%83%bc%e5%a4%89%e6%95%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r%e3%81%ae%e3%82%bf%e3%82%99%e3%83%9f%e3%83%bc%e5%a4%89%e6%95%b0\/","title":{"rendered":"R \u3067\u30c0\u30df\u30fc\u5909\u6570\u3092\u4f5c\u6210\u3059\u308b\u65b9\u6cd5 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u56de\u5e30\u30bf\u3099\u30df\u30fc\u5909\u6570\/\" target=\"_blank\" rel=\"noopener\">\u30c0\u30df\u30fc\u5909\u6570\u306f\u3001<\/a>\u56de\u5e30\u5206\u6790\u3067\u4f5c\u6210\u3059\u308b\u5909\u6570\u306e\u4e00\u7a2e\u3067\u3001\u30ab\u30c6\u30b4\u30ea\u30ab\u30eb\u5909\u6570\u3092 0 \u307e\u305f\u306f 1 \u306e 2 \u3064\u306e\u5024\u306e\u3044\u305a\u308c\u304b\u3092\u53d6\u308b\u6570\u5024\u5909\u6570\u3068\u3057\u3066\u8868\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u3042\u308a\u3001<em>\u5e74\u9f62<\/em>\u3068<em>\u5a5a\u59fb\u72b6\u6cc1<\/em>\u3092\u4f7f\u7528\u3057\u3066<em>\u53ce\u5165<\/em>\u3092\u4e88\u6e2c\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-13944 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/mannequin4.png\" alt=\"\" width=\"246\" height=\"309\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\"><em>\u5a5a\u59fb\u72b6\u6cc1\u3092<\/em>\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3059\u308b\u306b\u306f\u3001\u305d\u308c\u3092\u30c0\u30df\u30fc\u5909\u6570\u306b\u5909\u63db\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u73fe\u5728\u30013 \u3064\u306e\u7570\u306a\u308b\u5024 (\u300c\u72ec\u8eab\u300d\u3001\u300c\u65e2\u5a5a\u300d\u3001\u307e\u305f\u306f\u300c\u96e2\u5a5a\u300d) \u3092\u53d6\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u30ab\u30c6\u30b4\u30ea\u5909\u6570\u3067\u3042\u308b\u305f\u3081\u3001 <em>k<\/em> -1 = 3-1 = 2 \u500b\u306e\u30c0\u30df\u30fc\u5909\u6570\u3092\u4f5c\u6210\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c0\u30df\u30fc\u5909\u6570\u3092\u4f5c\u6210\u3059\u308b\u306b\u306f\u3001\u6700\u3082\u983b\u7e41\u306b\u8868\u793a\u3055\u308c\u308b\u300cSingle\u300d\u3092\u57fa\u672c\u5024\u3068\u3057\u3066\u305d\u306e\u307e\u307e\u306b\u3057\u3066\u304a\u304d\u307e\u3059\u3002\u3057\u305f\u304c\u3063\u3066\u3001<em>\u5a5a\u59fb\u72b6\u6cc1\u3092<\/em>\u30c0\u30df\u30fc\u5909\u6570\u306b\u5909\u63db\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-13950 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/mannequin6.png\" alt=\"\" width=\"615\" height=\"311\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u3067\u3053\u306e\u6b63\u78ba\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30c0\u30df\u30fc\u5909\u6570\u3092\u4f5c\u6210\u3057\u3001\u3053\u308c\u3089\u306e\u30c0\u30df\u30fc\u5909\u6570\u3092\u4e88\u6e2c\u5b50\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u56de\u5e30\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001R \u3067\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f5c\u6210\u3057\u307e\u3057\u3087\u3046\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame<\/span>\ndf &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (income=c(45000, 48000, 54000, 57000, 65000, 69000,\n                          78000, 83000, 98000, 104000, 107000),\n                 age=c(23, 25, 24, 29, 38, 36, 40, 59, 56, 64, 53),\n                 status=c('Single', 'Single', 'Single', 'Single',\n                          'Married', 'Single', 'Married', 'Divorced',\n                          'Divorced', 'Married', 'Married'))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\n   income age status\n1 45000 23 Single\n2 48000 25 Single\n3 54000 24 Single\n4 57000 29 Single\n5 65000 38 Married\n6 69000 36 Single\n7 78000 40 Married\n8 83000 59 Divorced\n9 98000 56 Divorced\n10 104000 64 Married\n11 107000 53 Married<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u30c0\u30df\u30fc\u5909\u6570\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001R \u306e<strong>ifelse()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30c0\u30df\u30fc\u5909\u6570\u3092\u5b9a\u7fa9\u3057\u3001\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u306b\u4f7f\u7528\u3059\u308b\u6700\u7d42\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092\u5b9a\u7fa9\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create dummy variables<\/span>\nmarried &lt;- <span style=\"color: #3366ff;\">ifelse<\/span> (df$status <span style=\"color: #993300;\">==<\/span> ' <span style=\"color: #008000;\">Married<\/span> ', 1, 0)\ndivorced &lt;- <span style=\"color: #3366ff;\">ifelse<\/span> (df$status <span style=\"color: #993300;\">==<\/span> ' <span style=\"color: #008000;\">Divorced<\/span> ', 1, 0)\n\n<span style=\"color: #008080;\">#create data frame to use for regression\n<\/span>df_reg &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (income = df$income,\n                     age = df$age,\n                     married = married,\n                     divorced = divorced)\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df_reg\n\n   income age married divorced\n1 45000 23 0 0\n2 48000 25 0 0\n3 54000 24 0 0\n4 57000 29 0 0\n5 65000 38 1 0\n6 69000 36 0 0\n7 78000 40 1 0\n8 83000 59 0 1\n9 98000 56 0 1\n10 104000 64 1 0\n11 107000 53 1 0\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u5f8c\u306b\u3001 <strong>lm()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create regression model\n<\/span>model &lt;- <span style=\"color: #3366ff;\">lm<\/span> (income ~ age + married + divorced, data=df_reg)\n\n<span style=\"color: #008080;\">#view regression model output\n<\/span>summary(model)\n\nCall:\nlm(formula = income ~ age + married + divorced, data = df_reg)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-9707.5 -5033.8 45.3 3390.4 12245.4 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)   \n(Intercept) 14276.1 10411.5 1.371 0.21266   \nage 1471.7 354.4 4.152 0.00428 **\nmarried 2479.7 9431.3 0.263 0.80018   \ndivorced -8397.4 12771.4 -0.658 0.53187   \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 8391 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<p><span style=\"color: #000000;\">\u8fd1\u4f3c\u3055\u308c\u305f\u56de\u5e30\u76f4\u7dda\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u53ce\u5165 = 14,276.1 + 1,471.7*(\u5e74\u9f62) + 2,479.7*(\u65e2\u5a5a) \u2013 8,397.4*(\u96e2\u5a5a)<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u65b9\u7a0b\u5f0f\u3092\u4f7f\u7528\u3057\u3066\u3001\u5e74\u9f62\u3068\u5a5a\u59fb\u72b6\u6cc1\u306b\u57fa\u3065\u3044\u3066\u500b\u4eba\u306e\u63a8\u5b9a\u53ce\u5165\u3092\u6c42\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u300135 \u6b73\u3067\u65e2\u5a5a\u8005\u306e\u63a8\u5b9a\u53ce\u5165\u306f<strong>68,264 \u30c9\u30eb<\/strong>\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u53ce\u5165 = 14,276.2 + 1,471.7*(35) + 2,479.7*(1) \u2013 8,397.4*(0) = 68,264 \u30c9\u30eb<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8868\u5185\u306e\u56de\u5e30\u4fc2\u6570\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u5207\u7247:<\/strong>\u5207\u7247\u306f\u30010 \u6b73\u306e\u72ec\u8eab\u8005\u306e\u5e73\u5747\u53ce\u5165\u3092\u8868\u3057\u307e\u3059\u3002\u660e\u3089\u304b\u306b\u3001\u30bc\u30ed\u5e74\u3092\u6301\u3064\u3053\u3068\u306f\u3067\u304d\u306a\u3044\u305f\u3081\u3001\u3053\u306e\u7279\u5b9a\u306e\u56de\u5e30\u30e2\u30c7\u30eb\u3067\u5207\u7247\u3092\u5358\u72ec\u3067\u89e3\u91c8\u3059\u308b\u3053\u3068\u306f\u610f\u5473\u304c\u3042\u308a\u307e\u305b\u3093\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u5e74\u9f62:<\/strong>\u5e74\u9f62\u304c\u4e0a\u304c\u308b\u3054\u3068\u306b\u3001\u53ce\u5165\u306f\u5e73\u5747 1,471.70 \u30c9\u30eb\u5897\u52a0\u3057\u307e\u3059\u3002 p \u5024 (0.004) \u306f 0.05 \u672a\u6e80\u3067\u3042\u308b\u305f\u3081\u3001\u5e74\u9f62\u306f\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u53ce\u5165\u306e\u4e88\u6e2c\u56e0\u5b50\u3067\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u65e2\u5a5a\u8005:<\/strong>\u65e2\u5a5a\u8005\u306e\u53ce\u5165\u306f\u3001\u72ec\u8eab\u8005\u3088\u308a\u3082\u5e73\u5747\u3067 2,479.70 \u30c9\u30eb\u9ad8\u304f\u306a\u308a\u307e\u3059\u3002 p \u5024 (0.800) \u306f 0.05 \u672a\u6e80\u3067\u306f\u306a\u3044\u305f\u3081\u3001\u3053\u306e\u5dee\u306f\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u96e2\u5a5a\u8005:<\/strong>\u96e2\u5a5a\u8005\u306e\u53ce\u5165\u306f\u3001\u72ec\u8eab\u8005\u3088\u308a\u5e73\u5747 8,397.40 \u30c9\u30eb\u5c11\u306a\u3044\u3067\u3059\u3002 p \u5024 (0.532) \u306f 0.05 \u672a\u6e80\u3067\u306f\u306a\u3044\u305f\u3081\u3001\u3053\u306e\u5dee\u306f\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3069\u3061\u3089\u306e\u30c0\u30df\u30fc\u5909\u6570\u3082\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u306f\u306a\u304b\u3063\u305f\u305f\u3081\u3001\u53ce\u5165\u306b\u4e88\u6e2c\u5024\u3092\u8ffd\u52a0\u3059\u308b\u3088\u3046\u306b\u306f\u898b\u3048\u306a\u3044\u305f\u3081\u3001<em>\u5a5a\u59fb\u72b6\u6cc1\u3092<\/em>\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u30e2\u30c7\u30eb\u304b\u3089\u524a\u9664\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30c0\u30df\u30fc\u5909\u6570\u306f\u3001\u56de\u5e30\u5206\u6790\u3067\u4f5c\u6210\u3059\u308b\u5909\u6570\u306e\u4e00\u7a2e\u3067\u3001\u30ab\u30c6\u30b4\u30ea\u30ab\u30eb\u5909\u6570\u3092 0 \u307e\u305f\u306f 1 \u306e 2 \u3064\u306e\u5024\u306e\u3044\u305a\u308c\u304b\u3092\u53d6\u308b\u6570\u5024\u5909\u6570\u3068\u3057\u3066\u8868\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002 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