{"id":2458,"date":"2023-07-22T04:43:11","date_gmt":"2023-07-22T04:43:11","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e5%8f%8c%e5%8f%98%e9%87%8f%e5%88%86%e6%9e%90\/"},"modified":"2023-07-22T04:43:11","modified_gmt":"2023-07-22T04:43:11","slug":"r%e4%b8%ad%e7%9a%84%e5%8f%8c%e5%8f%98%e9%87%8f%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e5%8f%8c%e5%8f%98%e9%87%8f%e5%88%86%e6%9e%90\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u6267\u884c\u53cc\u53d8\u91cf\u5206\u6790\uff08\u9644\u793a\u4f8b\uff09"},"content":{"rendered":"

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\u672f\u8bed\u53cc\u53d8\u91cf\u5206\u6790<\/strong>\u662f\u6307\u5bf9\u4e24\u4e2a\u53d8\u91cf\u7684\u5206\u6790\u3002\u4f60\u53ef\u4ee5\u8bb0\u4f4f\u8fd9\u4e00\u70b9\uff0c\u56e0\u4e3a\u524d\u7f00\u201cbi\u201d\u7684\u610f\u601d\u662f\u201c\u4e8c\u201d\u3002<\/span><\/p>\n

\u53cc\u53d8\u91cf\u5206\u6790\u7684\u76ee\u6807\u662f\u4e86\u89e3\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb<\/span><\/p>\n

\u6267\u884c\u53cc\u53d8\u91cf\u5206\u6790\u7684\u5e38\u7528\u65b9\u6cd5\u6709\u4ee5\u4e0b\u4e09\u79cd\uff1a<\/span><\/p>\n

1.<\/strong>\u70b9\u4e91<\/span><\/p>\n

2.<\/strong>\u76f8\u5173\u7cfb\u6570<\/span><\/p>\n

3.<\/strong>\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/span><\/p>\n

\u4ee5\u4e0b\u793a\u4f8b\u6f14\u793a\u5982\u4f55\u4f7f\u7528\u4ee5\u4e0b\u5305\u542b\u4e24\u4e2a\u53d8\u91cf\u4fe1\u606f\u7684\u6570\u636e\u96c6\u6267\u884c\u6bcf\u79cd\u7c7b\u578b\u7684\u53cc\u53d8\u91cf\u5206\u6790\uff1a (1)<\/strong>\u5b66\u4e60\u65f6\u95f4\u548c(2)<\/strong> 20 \u540d\u4e0d\u540c\u5b66\u751f\u83b7\u5f97\u7684\u6d4b\u8bd5\u6210\u7ee9\uff1a<\/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\u4e91<\/strong><\/span><\/h3>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u5728 R \u4e2d\u521b\u5efa\u5b66\u4e60\u65f6\u95f4\u4e0e\u8003\u8bd5\u6210\u7ee9\u7684\u6563\u70b9\u56fe\uff1a<\/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 \u8f74\u663e\u793a\u5b66\u4e60\u65f6\u95f4\uff0cy \u8f74\u663e\u793a\u8003\u8bd5\u6210\u7ee9\u3002<\/span><\/p>\n
\u8be5\u56fe\u663e\u793a\uff0c\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u5b58\u5728\u6b63\u76f8\u5173\u5173\u7cfb\uff1a\u968f\u7740\u5b66\u4e60\u65f6\u6570\u7684\u589e\u52a0\uff0c\u8003\u8bd5\u6210\u7ee9\u4e5f\u8d8b\u4e8e\u589e\u52a0\u3002<\/span><\/p>\n
 2. \u76f8\u5173\u7cfb\u6570<\/strong><\/span><\/h3>\n
\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\u662f\u4e00\u79cd\u91cf\u5316\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7ebf\u6027\u5173\u7cfb\u7684\u65b9\u6cd5\u3002<\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528R\u4e2d\u7684cor()<\/strong>\u51fd\u6570\u6765\u8ba1\u7b97\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u76ae\u5c14\u900a\u76f8\u5173\u7cfb\u6570\uff1a<\/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\u5173\u7cfb\u6570\u4e3a0.891<\/strong> \u3002<\/span><\/p>\n
\u8be5\u503c\u63a5\u8fd1 1\uff0c\u8868\u660e\u5b66\u4e60\u65f6\u95f4\u548c\u8003\u8bd5\u6210\u7ee9\u4e4b\u95f4\u5b58\u5728\u5f88\u5f3a\u7684\u6b63\u76f8\u5173\u5173\u7cfb\u3002<\/span><\/p>\n
 3. \u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/strong><\/span><\/h3>\n
\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u662f\u4e00\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u7528\u5b83\u6765\u627e\u5230\u6700\u201c\u9002\u5408\u201d\u4e00\u7ec4\u6570\u636e\u7684\u76f4\u7ebf\u65b9\u7a0b\uff0c\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u7528\u5b83\u6765\u7406\u89e3\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u786e\u5207\u5173\u7cfb\u3002<\/span><\/p>\n
\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 R \u4e2d\u7684lm()<\/strong>\u51fd\u6570\u6765\u62df\u5408\u5b66\u4e60\u65f6\u95f4\u548c\u6536\u5230\u7684\u8003\u8bd5\u7ed3\u679c\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\uff1a<\/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
\u62df\u5408\u540e\u7684\u56de\u5f52\u65b9\u7a0b\u4e3a\uff1a<\/span><\/p>\n
\u8003\u8bd5\u6210\u7ee9 = 69.0734 + 3.8471*\uff08\u5b66\u4e60\u65f6\u95f4\uff09<\/span><\/p>\n
\u8fd9\u544a\u8bc9\u6211\u4eec\uff0c\u6bcf\u591a\u5b66\u4e60\u4e00\u5c0f\u65f6\uff0c\u8003\u8bd5\u6210\u7ee9\u5c31\u4f1a\u5e73\u5747\u63d0\u9ad83.8471<\/strong> \u3002<\/span><\/p>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u62df\u5408\u56de\u5f52\u65b9\u7a0b\u6839\u636e\u5b66\u4e60\u7684\u603b\u5c0f\u65f6\u6570\u6765\u9884\u6d4b\u5b66\u751f\u5c06\u83b7\u5f97\u7684\u5206\u6570\u3002<\/span><\/p>\n
\u4f8b\u5982\uff0c\u5b66\u4e60 3 \u5c0f\u65f6\u7684\u5b66\u751f\u5e94\u8be5\u5f97\u523081.6147<\/strong>\u7684\u5206\u6570\uff1a<\/span><\/p>\n