{"id":2455,"date":"2023-07-22T04:43:11","date_gmt":"2023-07-22T04:43:11","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae%e4%ba%8c%e5%a4%89%e9%87%8f%e8%a7%a3%e6%9e%90\/"},"modified":"2023-07-22T04:43:11","modified_gmt":"2023-07-22T04:43:11","slug":"r-%e3%81%a6%e3%82%99%e3%81%ae%e4%ba%8c%e5%a4%89%e9%87%8f%e8%a7%a3%e6%9e%90","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae%e4%ba%8c%e5%a4%89%e9%87%8f%e8%a7%a3%e6%9e%90\/","title":{"rendered":"R \u3067\u4e8c\u5909\u91cf\u89e3\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 (\u4f8b\u4ed8\u304d)"},"content":{"rendered":"

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\u4e8c\u5909\u91cf\u5206\u6790<\/strong>\u3068\u3044\u3046\u7528\u8a9e\u306f\u30012 \u3064\u306e\u5909\u6570\u306e\u5206\u6790\u3092\u6307\u3057\u307e\u3059\u3002\u63a5\u982d\u8f9e\u300cbi\u300d\u306f\u300c2\u300d\u3092\u610f\u5473\u3059\u308b\u306e\u3067\u3001\u3053\u308c\u3092\u899a\u3048\u3066\u304a\u304f\u3068\u3088\u3044\u3067\u3057\u3087\u3046\u3002<\/span><\/p>\n

\u4e8c\u5909\u91cf\u89e3\u6790\u306e\u76ee\u6a19\u306f\u30012 \u3064\u306e\u5909\u6570\u9593\u306e\u95a2\u4fc2\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n

\u4e8c\u5909\u91cf\u89e3\u6790\u3092\u5b9f\u884c\u3059\u308b\u306b\u306f\u3001\u6b21\u306e 3 \u3064\u306e\u4e00\u822c\u7684\u306a\u65b9\u6cd5\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

1.<\/strong>\u70b9\u7fa4<\/span><\/p>\n

2.<\/strong>\u76f8\u95a2\u4fc2\u6570<\/span><\/p>\n

3.<\/strong>\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30<\/span><\/p>\n

\u6b21\u306e\u4f8b\u306f\u30012 \u3064\u306e\u5909\u6570(1)<\/strong>\u52c9\u5f37\u306b\u8cbb\u3084\u3057\u305f\u6642\u9593\u3001\u304a\u3088\u3073(2)<\/strong> 20 \u4eba\u306e\u7570\u306a\u308b\u5b66\u751f\u304c\u7372\u5f97\u3057\u305f\u30c6\u30b9\u30c8\u306e\u30b9\u30b3\u30a2\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u6b21\u306e\u30c7\u30fc\u30bf \u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u308c\u3089\u306e\u30bf\u30a4\u30d7\u306e\u4e8c\u5909\u91cf\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n

 #create data frame<\/span>\ndf <- data. frame<\/span> (hours=c(1, 1, 1, 2, 2, 2, 3, 3, 3, 3,\n                         3, 4, 4, 5, 5, 6, 6, 6, 7, 8),\n                 score=c(75, 66, 68, 74, 78, 72, 85, 82, 90, 82,\n                         80, 88, 85, 90, 92, 94, 94, 88, 91, 96))\n\n#view first six rows of data frame\n<\/span>head(df)\n\n  hours score\n1 1 75\n2 1 66\n3 1 68\n4 2 74\n5 2 78\n6 2 72<\/strong><\/pre>\n
 1. \u70b9\u7fa4<\/strong><\/span><\/h3>\n
\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001R \u3067\u5b66\u7fd2\u6642\u9593\u3068\u8a66\u9a13\u306e\u6210\u7e3e\u306e\u6563\u5e03\u56f3\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span> <\/p>\n
 #create scatterplot of hours studied vs. exam score<\/span>\nplot(df$hours, df$score, pch= 16<\/span> , col=' steelblue<\/span> ',\n     main=' Hours Studied vs. Exam Score<\/span> ',\n     xlab=' Hours Studied<\/span> ', ylab=' Exam Score<\/span> ')\n<\/strong><\/pre>\n
\"\"<\/p>\n
 X \u8ef8\u306f\u5b66\u7fd2\u6642\u9593\u3092\u793a\u3057\u3001Y \u8ef8\u306f\u8a66\u9a13\u3067\u7372\u5f97\u3057\u305f\u6210\u7e3e\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u30b0\u30e9\u30d5\u306f\u30012 \u3064\u306e\u5909\u6570\u306e\u9593\u306b\u6b63\u306e\u95a2\u4fc2\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u5b66\u7fd2\u6642\u9593\u6570\u304c\u5897\u52a0\u3059\u308b\u306b\u3064\u308c\u3066\u3001\u8a66\u9a13\u306e\u30b9\u30b3\u30a2\u3082\u5897\u52a0\u3059\u308b\u50be\u5411\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n
 2. \u76f8\u95a2\u4fc2\u6570<\/strong><\/span><\/h3>\n
\u30d4\u30a2\u30bd\u30f3\u76f8\u95a2\u4fc2\u6570\u306f\u30012 \u3064\u306e\u5909\u6570\u9593\u306e\u7dda\u5f62\u95a2\u4fc2\u3092\u5b9a\u91cf\u5316\u3059\u308b\u65b9\u6cd5\u3067\u3059\u3002<\/span><\/p>\n
 R \u306ecor()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30012 \u3064\u306e\u5909\u6570\u9593\u306e\u30d4\u30a2\u30bd\u30f3\u76f8\u95a2\u4fc2\u6570\u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #calculate correlation between hours studied and exam score received\n<\/span>cor(df$hours, df$score)\n\n[1] 0.891306\n<\/strong><\/pre>\n
\u76f8\u95a2\u4fc2\u6570\u306f0.891<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u3053\u306e\u5024\u306f 1 \u306b\u8fd1\u304f\u3001\u5b66\u7fd2\u6642\u9593\u3068\u8a66\u9a13\u306e\u6210\u7e3e\u306e\u9593\u306b\u5f37\u3044\u6b63\u306e\u76f8\u95a2\u304c\u3042\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 3. \u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30<\/strong><\/span><\/h3>\n
\u5358\u7d14\u7dda\u5f62\u56de\u5e30\u306f\u3001\u4e00\u9023\u306e\u30c7\u30fc\u30bf\u306b\u6700\u3082\u3088\u304f\u300c\u9069\u5408\u300d\u3059\u308b\u76f4\u7dda\u306e\u65b9\u7a0b\u5f0f\u3092\u898b\u3064\u3051\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u7d71\u8a08\u624b\u6cd5\u3067\u3042\u308a\u3001\u3053\u308c\u3092\u4f7f\u7528\u3057\u3066 2 \u3064\u306e\u5909\u6570\u9593\u306e\u6b63\u78ba\u306a\u95a2\u4fc2\u3092\u7406\u89e3\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 R \u306elm()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u5b66\u7fd2\u6642\u9593\u3068\u53d7\u3051\u53d6\u3063\u305f\u8a66\u9a13\u7d50\u679c\u306b\u5bfe\u3059\u308b\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u3092\u5f53\u3066\u306f\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 #fit simple linear regression model\n<\/span>fit <- lm(score ~ hours, data=df)\n\n#view summary of model\n<\/span>summary(fit)\n\nCall:\nlm(formula = score ~ hours, data = df)\n\nResiduals:\n   Min 1Q Median 3Q Max \n-6,920 -3,927 1,309 1,903 9,385 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 69.0734 1.9651 35.15 < 2nd-16 ***\nhours 3.8471 0.4613 8.34 1.35e-07 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 4.171 on 18 degrees of freedom\nMultiple R-squared: 0.7944, Adjusted R-squared: 0.783 \nF-statistic: 69.56 on 1 and 18 DF, p-value: 1.347e-07<\/strong><\/pre>\n
\u8fd1\u4f3c\u3055\u308c\u305f\u56de\u5e30\u5f0f\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u8a66\u9a13\u30b9\u30b3\u30a2 = 69.0734 + 3.8471*(\u52c9\u5f37\u6642\u9593)<\/span><\/p>\n
\u3053\u308c\u306f\u3001\u5b66\u7fd2\u6642\u9593\u304c\u8ffd\u52a0\u3055\u308c\u308b\u3054\u3068\u306b\u3001\u8a66\u9a13\u30b9\u30b3\u30a2\u304c\u5e73\u57473.8471<\/strong>\u5897\u52a0\u3059\u308b\u3053\u3068\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u307e\u305f\u3001\u9069\u5408\u56de\u5e30\u5f0f\u3092\u4f7f\u7528\u3057\u3066\u3001\u5408\u8a08\u5b66\u7fd2\u6642\u9593\u6570\u306b\u57fa\u3065\u3044\u3066\u751f\u5f92\u304c\u53d7\u3051\u53d6\u308b\u30b9\u30b3\u30a2\u3092\u4e88\u6e2c\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u305f\u3068\u3048\u3070\u30013 \u6642\u9593\u52c9\u5f37\u3057\u305f\u751f\u5f92\u306f81.6147<\/strong>\u306e\u30b9\u30b3\u30a2\u3092\u53d6\u5f97\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n