{"id":490,"date":"2023-07-29T17:42:56","date_gmt":"2023-07-29T17:42:56","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e4%b8%80%e5%85%83%e9%85%8d%e7%bd%ae%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90\/"},"modified":"2023-07-29T17:42:56","modified_gmt":"2023-07-29T17:42:56","slug":"%e4%b8%80%e5%85%83%e9%85%8d%e7%bd%ae%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e4%b8%80%e5%85%83%e9%85%8d%e7%bd%ae%e5%88%86%e6%95%a3%e5%88%86%e6%9e%90\/","title":{"rendered":"R \u3067\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u4e00\u65b9\u5411\u5206\u6563\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306f\u3001<\/a> 3 \u3064\u4ee5\u4e0a\u306e\u72ec\u7acb\u3057\u305f\u30b0\u30eb\u30fc\u30d7\u306e\u5e73\u5747\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30bf\u30a4\u30d7\u306e\u691c\u5b9a\u306f\u3001\u5fdc\u7b54\u5909\u6570\u306b\u5bfe\u3059\u308b\u4e88\u6e2c\u5909\u6570<em>\u306e<\/em>\u5f71\u97ff\u3092\u5206\u6790\u3059\u308b\u305f\u3081\u3001<em>\u4e00\u5143<\/em>\u914d\u7f6e\u5206\u6563\u5206\u6790\u3068\u547c\u3070\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8<\/strong>: \u5fdc\u7b54\u5909\u6570\u306b\u5bfe\u3059\u308b 2 \u3064\u306e\u4e88\u6e2c\u5909\u6570\u306e\u5f71\u97ff\u306b\u8208\u5473\u304c\u3042\u308b\u5834\u5408\u306f\u3001<a href=\"https:\/\/statorials.org\/ja\/\u5206\u6563\u5206\u6790\u53cc\u65b9\u5411\/\" target=\"_blank\" rel=\"noopener\">\u4e8c\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790<\/a>\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>R \u3067\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001R \u3067\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u80cc\u666f<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">3 \u3064\u306e\u7570\u306a\u308b\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0\u304c\u6e1b\u91cf\u306b\u7570\u306a\u308b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002\u79c1\u305f\u3061\u304c\u7814\u7a76\u3059\u308b\u4e88\u6e2c\u5909\u6570\u306f<em>\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0<\/em>\u3067\u3042\u308a\u3001<a href=\"https:\/\/statorials.org\/ja\/\u5909\u6570\u306e\u8aac\u660e\u5fdc\u7b54\/\" target=\"_blank\" rel=\"noopener\">\u5fdc\u7b54\u5909\u6570\u306f<\/a>\u30dd\u30f3\u30c9\u5358\u4f4d\u3067\u6e2c\u5b9a\u3055\u308c\u308b<em>\u4f53\u91cd\u6e1b\u5c11<\/em>\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u5b9f\u884c\u3057\u3066\u30013 \u3064\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3088\u308b\u4f53\u91cd\u6e1b\u5c11\u306e\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b9f\u9a13\u306b\u53c2\u52a0\u3059\u308b\u4eba\u3092 90 \u4eba\u52df\u96c6\u3057\u300130 \u4eba\u3092\u30e9\u30f3\u30c0\u30e0\u306b\u5272\u308a\u5f53\u3066\u3066\u3001\u30d7\u30ed\u30b0\u30e9\u30e0 A\u3001\u30d7\u30ed\u30b0\u30e9\u30e0 B\u3001\u307e\u305f\u306f\u30d7\u30ed\u30b0\u30e9\u30e0 C \u306e\u3044\u305a\u308c\u304b\u3092 1 \u304b\u6708\u9593\u5b9f\u884c\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u4f5c\u696d\u3059\u308b\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<span style=\"color: #000000;\">set.seed(0)\n<\/span>\n#create data frame\n<span style=\"color: #000000;\">data &lt;- data.frame(program = rep(c(\"A\", \"B\", \"C\"), each = 30),\n                   weight_loss = c(runif(30, 0, 3),\n                                   runif(30, 0, 5),\n                                   runif(30, 1, 7)))<\/span>\n\n#view first six rows of data frame\n<span style=\"color: #000000;\">head(data)\n<\/span>\n<span style=\"color: #000000;\"># program weight_loss\n#1 A 2.6900916\n#2 A 0.7965260\n#3 A 1.1163717\n#4 A 1.7185601\n#5 A 2.7246234\n#6 A 0.6050458<\/span>\n<\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30c7\u30fc\u30bf \u30d5\u30ec\u30fc\u30e0\u306e\u6700\u521d\u306e\u5217\u306f\u3001\u305d\u306e\u4eba\u304c 1 \u304b\u6708\u9593\u53c2\u52a0\u3057\u305f\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u793a\u3057\u30012 \u756a\u76ee\u306e\u5217\u306f\u3001\u305d\u306e\u4eba\u304c\u30d7\u30ed\u30b0\u30e9\u30e0\u7d42\u4e86\u6642\u306b\u7d4c\u9a13\u3057\u305f\u7dcf\u4f53\u91cd\u6e1b\u5c11\u3092\u30dd\u30f3\u30c9\u5358\u4f4d\u3067\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30c7\u30fc\u30bf\u3092\u63a2\u7d22\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4e00\u5143\u914d\u7f6e ANOVA \u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b\u524d\u306b\u3001 <strong>dplyr<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066 3 \u3064\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u305d\u308c\u305e\u308c\u306e\u4f53\u91cd\u6e1b\u5c11\u306e\u5e73\u5747\u3068\u6a19\u6e96\u504f\u5dee\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u3092\u3088\u308a\u3088\u304f\u7406\u89e3\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>dplyr<\/em> package<\/span>\n<span style=\"color: #008000;\">library<\/span> (dplyr)\n\n<span style=\"color: #008080;\">#find mean and standard deviation of weight loss for each treatment group<\/span>\ndata %&gt;%\n  <span style=\"color: #800080;\">group_by<\/span> (program) %&gt;%\n  <span style=\"color: #800080;\">summarize<\/span> (mean = mean(weight_loss),\n            sd = sd(weight_loss))\n\n# A tibble: 3 x 3\n# program mean sd\n#      \n#1 A 1.58 0.905\n#2 B 2.56 1.24 \n#3 C 4.13 1.57  \n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">3 \u3064\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u3054\u3068\u306b<a href=\"https:\/\/statorials.org\/ja\/-10\/\" target=\"_blank\" rel=\"noopener\">\u7bb1\u3072\u3052\u56f3<\/a>\u3092\u4f5c\u6210\u3057\u3066\u3001\u5404\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u4f53\u91cd\u6e1b\u5c11\u306e\u5206\u5e03\u3092\u8996\u899a\u5316\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create boxplots\n<\/span>boxplot(weight_loss ~ program,\ndata = data,\nmain = \"Weight Loss Distribution by Program\",\nxlab = \"Program\",\nylab = \"Weight Loss\",\ncol = \"steelblue\",\nborder = \"black\")<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089\u306e\u7bb1\u3072\u3052\u56f3\u304b\u3089\u3001\u30d7\u30ed\u30b0\u30e9\u30e0 C \u306e\u53c2\u52a0\u8005\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u304c\u6700\u3082\u9ad8\u304f\u3001\u30d7\u30ed\u30b0\u30e9\u30e0 A \u306e\u53c2\u52a0\u8005\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u304c\u6700\u3082\u4f4e\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001\u4f53\u91cd\u6e1b\u5c11\u306e\u6a19\u6e96\u504f\u5dee (\u7bb1\u3072\u3052\u56f3\u306e\u300c\u9577\u3055\u300d) \u304c\u3001\u30d7\u30ed\u30b0\u30e9\u30e0 C \u3067\u306f\u4ed6\u306e 2 \u3064\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u6bd4\u3079\u3066\u5c11\u3057\u9ad8\u3044\u3053\u3068\u3082\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u306b\u5f53\u3066\u306f\u3081\u3066\u3001\u3053\u308c\u3089\u306e\u8996\u899a\u7684\u306a\u9055\u3044\u304c\u5b9f\u969b\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u30e2\u30c7\u30eb \u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">R \u3067\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306e\u4e00\u822c\u7684\u306a\u69cb\u6587\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<p style=\"text-align: left;\"> <strong><span style=\"color: #000000;\">aov(\u5fdc\u7b54\u5909\u6570 ~ \u4e88\u6e2c\u5909\u6570\u3001\u30c7\u30fc\u30bf = \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8)<\/span><\/strong><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001 <em>weight_loss \u3092<\/em>\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001<em>\u30d7\u30ed\u30b0\u30e9\u30e0\u3092<\/em>\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001\u4e00\u5143\u914d\u7f6e ANOVA \u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u6b21\u306b\u3001 <strong>summary()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u3092\u8868\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit the one-way ANOVA model<\/span>\nmodel &lt;- aov(weight_loss ~ program, data = data)\n\n<span style=\"color: #008080;\">#view the model output<\/span>\nsummary(model)\n\n# Df Sum Sq Mean Sq F value Pr(&gt;F)    \n#program 2 98.93 49.46 30.83 7.55e-11 ***\n#Residuals 87 139.57 1.60                     \n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u304b\u3089\u3001\u4e88\u6e2c\u5909\u6570\u306e<em>\u30d7\u30ed\u30b0\u30e9\u30e0\u306f<\/em>0.05 \u6709\u610f\u6c34\u6e96\u3067\u7d71\u8a08\u7684\u306b\u6709\u610f\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8a00\u3044\u63db\u3048\u308c\u3070\u30013 \u3064\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u3088\u308b\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u306e\u9593\u306b\u306f\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u3068\u3044\u3046\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30e2\u30c7\u30eb\u306e\u4eee\u5b9a\u3092\u78ba\u8a8d\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u9032\u3080\u524d\u306b\u3001\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u304c\u4fe1\u983c\u3067\u304d\u308b\u3088\u3046\u306b\u3001\u30e2\u30c7\u30eb\u306e<a href=\"https:\/\/statorials.org\/ja\/\u30bf\u3099\u30ce\u30cf\u3099\u4eee\u8aac\/\" target=\"_blank\" rel=\"noopener\">\u4eee\u5b9a\u304c<\/a>\u6e80\u305f\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u7279\u306b\u3001\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3067\u306f\u6b21\u306e\u3053\u3068\u3092\u524d\u63d0\u3068\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u72ec\u7acb\u6027<\/strong>\u2013 \u5404\u30b0\u30eb\u30fc\u30d7\u306e\u89b3\u5bdf\u306f\u4e92\u3044\u306b\u72ec\u7acb\u3057\u3066\u3044\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span>\u30e9\u30f3\u30c0\u30e0\u5316\u3055\u308c\u305f\u8a2d\u8a08<span style=\"color: #000000;\">\u3092\u4f7f\u7528\u3057\u305f\u305f\u3081<\/span><span style=\"color: #000000;\">(\u3064\u307e\u308a\u3001\u53c2\u52a0\u8005\u3092\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0\u306b\u30e9\u30f3\u30c0\u30e0\u306b\u5272\u308a\u5f53\u3066\u307e\u3057\u305f)\u3001\u3053\u306e\u4eee\u5b9a\u306f\u6e80\u305f\u3055\u308c\u308b\u306f\u305a\u306a\u306e\u3067\u3001\u3042\u307e\u308a\u5fc3\u914d\u3059\u308b\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. \u6b63\u898f\u6027<\/strong>\u2013 \u5f93\u5c5e\u5909\u6570\u306f\u3001\u4e88\u6e2c\u5909\u6570\u306e\u5404\u30ec\u30d9\u30eb\u306b\u5bfe\u3057\u3066\u307b\u307c\u6b63\u898f\u5206\u5e03\u3092\u6301\u3064\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3. \u7b49\u5206\u6563<\/strong>\u2013 \u5404\u30b0\u30eb\u30fc\u30d7\u306e\u5206\u6563\u306f\u7b49\u3057\u3044\u304b\u307b\u307c\u7b49\u3057\u3044\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6b63\u898f\u6027<\/strong>\u3068<strong>\u7b49\u5206\u6563<\/strong>\u306e\u4eee\u5b9a\u3092\u30c1\u30a7\u30c3\u30af\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001 <strong>plot()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u306e\u95a2\u6570\u306f\u30014 \u3064\u306e\u30e2\u30c7\u30eb\u691c\u67fb\u30d7\u30ed\u30c3\u30c8\u3092\u751f\u6210\u3057\u307e\u3059\u3002\u7279\u306b\u3001\u6b21\u306e 2 \u3064\u306e\u30d7\u30ed\u30c3\u30c8\u306b\u7279\u306b\u95a2\u5fc3\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u6b8b\u5dee vs.\u8fd1\u4f3c<\/strong>\u2013 \u3053\u306e\u30b0\u30e9\u30d5\u306f\u3001\u6b8b\u5dee\u3068\u8fd1\u4f3c\u5024\u306e\u95a2\u4fc2\u3092\u793a\u3057\u307e\u3059\u3002\u3053\u306e\u30b0\u30e9\u30d5\u3092\u4f7f\u7528\u3057\u3066\u3001\u30b0\u30eb\u30fc\u30d7\u9593\u306e\u5206\u6563\u304c\u307b\u307c\u7b49\u3057\u3044\u304b\u3069\u3046\u304b\u3092\u5927\u307e\u304b\u306b\u8a55\u4fa1\u3067\u304d\u307e\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>QQ \u30d7\u30ed\u30c3\u30c8<\/strong>\u2013 \u3053\u306e\u30d7\u30ed\u30c3\u30c8\u306b\u306f\u3001\u7406\u8ad6\u7684\u306a\u5206\u4f4d\u6570\u306b\u5bfe\u3059\u308b\u6a19\u6e96\u5316\u3055\u308c\u305f\u6b8b\u5dee\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002\u3053\u306e\u30b0\u30e9\u30d5\u3092\u4f7f\u7528\u3057\u3066\u3001\u6b63\u898f\u6027\u306e\u4eee\u5b9a\u304c\u6e80\u305f\u3055\u308c\u3066\u3044\u308b\u304b\u3069\u3046\u304b\u3092\u5927\u307e\u304b\u306b\u8a55\u4fa1\u3067\u304d\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u308c\u3089\u306e\u30e2\u30c7\u30eb\u691c\u67fb\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #000000;\">plot(model)<\/span><\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u4e0a\u306e<em>QQ \u30b0\u30e9\u30d5\u3092\u4f7f\u7528<\/em>\u3059\u308b\u3068\u3001\u6b63\u898f\u6027\u306e\u4eee\u5b9a\u3092\u691c\u8a3c\u3067\u304d\u307e\u3059\u3002\u7406\u60f3\u7684\u306b\u306f\u3001\u6a19\u6e96\u5316\u3055\u308c\u305f\u6b8b\u5dee\u306f\u30d7\u30ed\u30c3\u30c8\u306e\u76f4\u7dda\u306e\u5bfe\u89d2\u7dda\u306b\u6cbf\u3063\u3066\u914d\u7f6e\u3055\u308c\u307e\u3059\u3002\u305f\u3060\u3057\u3001\u4e0a\u306e\u30b0\u30e9\u30d5\u3067\u306f\u3001\u6700\u521d\u3068\u6700\u5f8c\u306b\u5411\u304b\u3063\u3066\u6b8b\u5dee\u304c\u7dda\u304b\u3089\u5c11\u3057\u305a\u308c\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u3001\u6b63\u898f\u6027\u306e\u4eee\u5b9a\u304c\u9055\u53cd\u3055\u308c\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30b6<em>\u30fb\u30ec\u30b8\u30c7\u30e5\u30a2\u30eb\u30ba vs.<\/em>\u4e0a\u306e<em>\u8abf\u6574\u3055\u308c\u305f\u30b0\u30e9\u30d5\u306b\u3088\u308a\u3001<\/em>\u5206\u6563\u304c\u7b49\u3057\u3044\u3068\u3044\u3046\u4eee\u5b9a\u3092\u691c\u8a3c\u3067\u304d\u307e\u3059\u3002\u7406\u60f3\u7684\u306b\u306f\u3001\u8fd1\u4f3c\u5024\u306e\u5404\u30ec\u30d9\u30eb\u3067\u6b8b\u5dee\u304c\u5747\u7b49\u306b\u5206\u6563\u3055\u308c\u308b\u3053\u3068\u304c\u671b\u307e\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd1\u4f3c\u5024\u304c\u9ad8\u304f\u306a\u308b\u307b\u3069\u6b8b\u5dee\u304c\u3055\u3089\u306b\u5e83\u304c\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u3001<a href=\"https:\/\/statorials.org\/ja\/\u7b49\u5206\u6563\u4eee\u5b9a\/\" target=\"_blank\" rel=\"noopener\">\u5206\u6563\u304c\u7b49\u3057\u3044\u3068\u3044\u3046\u4eee\u5b9a\u306b<\/a>\u9055\u53cd\u3057\u3066\u3044\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7b49\u5206\u6563\u3092\u6b63\u5f0f\u306b\u30c6\u30b9\u30c8\u3059\u308b\u306b\u306f\u3001 <strong>car<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066 Levene \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load car package\n<\/span><span style=\"color: #008000;\">library<\/span> (car)\n\n<span style=\"color: #008080;\">#conduct Levene's Test for equality of variances\n<\/span>leveneTest(weight_loss ~ program, data = data)\n\n#Levene's Test for Homogeneity of Variance (center = median)\n# Df F value Pr(&gt;F)  \n#group 2 4.1716 0.01862 *\n#87                  \n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u691c\u5b9a\u306e p \u5024\u306f<strong>0.01862<\/strong>\u3067\u3059\u3002\u6709\u610f\u6c34\u6e96 0.05 \u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u5206\u6563\u304c 3 \u3064\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u9593\u3067\u7b49\u3057\u3044\u3068\u3044\u3046\u5e30\u7121\u4eee\u8aac\u304c\u68c4\u5374\u3055\u308c\u307e\u3059\u3002\u305f\u3060\u3057\u3001\u6709\u610f\u6c34\u6e96 0.01 \u3092\u4f7f\u7528\u3059\u308b\u3068\u3001\u5e30\u7121\u4eee\u8aac\u306f\u68c4\u5374\u3055\u308c\u307e\u305b\u3093\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b63\u898f\u6027\u3068\u5206\u6563\u306e\u7b49\u4fa1\u6027\u306e\u4eee\u5b9a\u304c\u78ba\u5b9f\u306b\u6e80\u305f\u3055\u308c\u308b\u3088\u3046\u306b\u30c7\u30fc\u30bf\u306e\u5909\u63db\u3092\u8a66\u307f\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u304c\u3001\u4eca\u306e\u3068\u3053\u308d\u306f\u3042\u307e\u308a\u5fc3\u914d\u3057\u307e\u305b\u3093\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u6cbb\u7642\u6cd5\u306e\u9055\u3044\u3092\u5206\u6790\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306e\u4eee\u5b9a\u304c\u6e80\u305f\u3055\u308c\u3066\u3044\u308b (\u307e\u305f\u306f\u5408\u7406\u7684\u306b\u6e80\u305f\u3055\u308c\u3066\u3044\u308b) \u3053\u3068\u3092\u78ba\u8a8d\u3057\u305f\u3089\u3001<a href=\"https:\/\/statorials.org\/ja\/-10\/\" target=\"_blank\" rel=\"noopener\">\u4e8b\u5f8c\u30c6\u30b9\u30c8\u3092<\/a>\u5b9f\u884c\u3057\u3066\u3001\u3069\u306e\u6cbb\u7642\u30b0\u30eb\u30fc\u30d7\u304c\u4e92\u3044\u306b\u7570\u306a\u308b\u304b\u3092\u6b63\u78ba\u306b\u5224\u65ad\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e8b\u5f8c\u30c6\u30b9\u30c8\u3067\u306f\u3001 <strong>TukeyHSD()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u591a\u91cd\u6bd4\u8f03\u306e Tukey \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#perform Tukey's Test for multiple comparisons\n<\/span>TukeyHSD(model, conf.level=.95) \n\n#Tukey multiple comparisons of means\n# 95% family-wise confidence level\n#\n#Fit: aov(formula = weight_loss ~ program, data = data)\n#\n#$program\n# diff lwr upr p adj\n#BA 0.9777414 0.1979466 1.757536 0.0100545\n#CA 2.5454024 1.7656076 3.325197 0.0000000\n#CB 1.5676610 0.7878662 2.347456 0.0000199\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">p \u5024\u306f\u3001\u5404\u30d7\u30ed\u30b0\u30e9\u30e0\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u793a\u3057\u307e\u3059\u3002\u7d50\u679c\u306f\u3001\u5404\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u306e\u9593\u306b 0.05 \u6709\u610f\u6c34\u6e96\u3067\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">R \u306e<strong>Lot(TukeyHSD())<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001Tukey \u691c\u5b9a\u306e\u7d50\u679c\u3068\u3057\u3066\u5f97\u3089\u308c\u308b 95% \u4fe1\u983c\u533a\u9593\u3092\u8996\u899a\u5316\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create confidence interval for each comparison\n<\/span>plot(TukeyHSD(model, conf.level=.95), las = 2)\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4fe1\u983c\u533a\u9593\u306e\u7d50\u679c\u306f\u4eee\u8aac\u691c\u5b9a\u306e\u7d50\u679c\u3068\u4e00\u81f4\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7279\u306b\u3001\u30d7\u30ed\u30b0\u30e9\u30e0\u9593\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u306e\u4fe1\u983c\u533a\u9593\u306b\u306f\u5024<em>0<\/em>\u304c\u542b\u307e\u308c\u3066\u3044\u306a\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u30013 \u3064\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u9593\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u3001<a href=\"https:\/\/statorials.org\/ja\/\u4eee\u8aac\u691c\u8a3c1\/\" target=\"_blank\" rel=\"noopener\">\u4eee\u8aac\u691c\u5b9a<\/a>\u306e\u3059\u3079\u3066\u306e<a href=\"https:\/\/statorials.org\/ja\/p-\u5024\u306e\u7d71\u8a08\u7684\u6709\u610f\u6027\/\" target=\"_blank\" rel=\"noopener\">p \u5024\u304c<\/a>0.05 \u672a\u6e80\u3067\u3042\u308b\u3053\u3068\u3068\u4e00\u81f4\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><strong><span style=\"color: #000000;\">\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u7d50\u679c\u306e\u30ec\u30dd\u30fc\u30c8<\/span><\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u5f8c\u306b\u3001\u7d50\u679c\u3092\u8981\u7d04\u3057\u305f\u65b9\u6cd5\u3067\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306e\u7d50\u679c\u3092\u30ec\u30dd\u30fc\u30c8\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u904b\u52d5\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u52b9\u679c\u3092\u8abf\u3079\u308b\u305f\u3081\u306b\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u304c\u5b9f\u884c\u3055\u308c\u307e\u3057\u305f\u3002 <em>&nbsp;<\/em>\u4f53\u91cd\u6e1b\u5c11<em>\uff08\u30dd\u30f3\u30c9\u5358\u4f4d\u3067\u6e2c\u5b9a\uff09\u3002<\/em>\u4f53\u91cd\u6e1b\u5c11\u306b\u5bfe\u3059\u308b 3 \u3064\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u52b9\u679c\u306e\u9593\u306b\u306f\u3001\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308a\u307e\u3057\u305f (F(2, 87) = 30.83\u3001p = 7.55e-11)\u3002<\/span><span style=\"color: #000000;\">\u4e8b\u5f8c Tukey \u306e HSD \u30c6\u30b9\u30c8\u3092\u5b9f\u884c\u3057\u307e\u3057\u305f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30d7\u30ed\u30b0\u30e9\u30e0 C \u306e\u53c2\u52a0\u8005\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u306f\u3001\u30d7\u30ed\u30b0\u30e9\u30e0 B \u306e\u53c2\u52a0\u8005\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u3088\u308a\u3082\u5927\u5e45\u306b\u5927\u304d\u304f\u306a\u3063\u3066\u3044\u307e\u3059 (p &lt; 0.0001)\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30d7\u30ed\u30b0\u30e9\u30e0 C \u306e\u53c2\u52a0\u8005\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u306f\u3001\u30d7\u30ed\u30b0\u30e9\u30e0 A \u306e\u53c2\u52a0\u8005\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u3088\u308a\u3082\u5927\u5e45\u306b\u5927\u304d\u304f\u306a\u3063\u3066\u3044\u307e\u3059 (p &lt; 0.0001)\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3055\u3089\u306b\u3001\u30d7\u30ed\u30b0\u30e9\u30e0 B \u306e\u53c2\u52a0\u8005\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u306f\u3001\u30d7\u30ed\u30b0\u30e9\u30e0 A \u306e\u53c2\u52a0\u8005\u306e\u5e73\u5747\u4f53\u91cd\u6e1b\u5c11\u3088\u308a\u3082\u6709\u610f\u306b\u5927\u304d\u304b\u3063\u305f (p = 0.01)\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306b\u95a2\u3059\u308b\u8ffd\u52a0\u60c5\u5831\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/ja\/\u4e00\u65b9\u5411\u5206\u6563\u5206\u6790\/\" target=\"_blank\" rel=\"noopener\">\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306e\u6982\u8981<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u4e8b\u5f8c\u5206\u6563\u5206\u6790\u30c6\u30b9\u30c8\/\" target=\"_blank\" rel=\"noopener\">ANOVA \u3067\u4e8b\u5f8c\u30c6\u30b9\u30c8\u3092\u4f7f\u7528\u3059\u308b\u305f\u3081\u306e\u30ac\u30a4\u30c9<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u5206\u6563\u5206\u6790\u7d50\u679c\u3092\u5831\u544a\u3059\u308b\u65b9\u6cd5\/\" target=\"_blank\" rel=\"noopener\">\u5b8c\u5168\u30ac\u30a4\u30c9: ANOVA \u7d50\u679c\u3092\u5831\u544a\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u306f\u3001 3 \u3064\u4ee5\u4e0a\u306e\u72ec\u7acb\u3057\u305f\u30b0\u30eb\u30fc\u30d7\u306e\u5e73\u5747\u9593\u306b\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002 \u3053\u306e\u30bf\u30a4\u30d7\u306e\u691c\u5b9a\u306f\u3001\u5fdc\u7b54\u5909\u6570\u306b\u5bfe\u3059\u308b\u4e88\u6e2c\u5909\u6570\u306e\u5f71\u97ff\u3092\u5206\u6790\u3059\u308b\u305f\u3081\u3001\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3068\u547c\u3070\u308c\u307e\u3059\u3002  [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-490","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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