{"id":2009,"date":"2023-07-24T02:50:02","date_gmt":"2023-07-24T02:50:02","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e7%82%b9%e4%bc%b0%e8%ae%a1\/"},"modified":"2023-07-24T02:50:02","modified_gmt":"2023-07-24T02:50:02","slug":"r-%e4%b8%ad%e7%9a%84%e7%82%b9%e4%bc%b0%e8%ae%a1","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e7%9a%84%e7%82%b9%e4%bc%b0%e8%ae%a1\/","title":{"rendered":"\u5982\u4f55\u5728 r \u4e2d\u8ba1\u7b97\u70b9\u4f30\u8ba1\uff08\u9644\u793a\u4f8b\uff09"},"content":{"rendered":"

<\/p>\n

\n

\u70b9\u4f30\u8ba1<\/a>\u8868\u793a\u6211\u4eec\u6839\u636e\u6837\u672c\u6570\u636e\u8ba1\u7b97\u51fa\u7684\u6570\u5b57\uff0c\u4ee5\u4f30\u8ba1\u603b\u4f53\u53c2\u6570\u3002\u8fd9\u662f\u6211\u4eec\u5bf9\u771f\u5b9e\u603b\u4f53\u53c2\u6570\u53ef\u80fd\u7684\u6700\u4f73\u4f30\u8ba1\u3002<\/span><\/p>\n

\u4e0b\u8868\u663e\u793a\u4e86\u6211\u4eec\u7528\u6765\u4f30\u8ba1\u603b\u4f53\u53c2\u6570\u7684\u70b9\u4f30\u8ba1\uff1a<\/span><\/p>\n\n\n\n\n\n
\u7684\u63aa\u65bd<\/strong><\/span><\/th>\n\u4eba\u53e3\u53c2\u6570<\/strong><\/span><\/th>\n\u70b9\u4f30\u8ba1<\/strong><\/span><\/th>\n<\/tr>\n
\u610f\u601d\u662f<\/span><\/td>\n\u03bc\uff08\u603b\u4f53\u5e73\u5747\u503c\uff09<\/span><\/td>\n x<\/span> \uff08\u6837\u672c\u5e73\u5747\u503c\uff09<\/span><\/td>\n<\/tr>\n
\u90e8\u5206<\/span><\/td>\n\u03c0\uff08\u4eba\u53e3\u6bd4\u4f8b\uff09<\/span><\/td>\n p\uff08\u6837\u672c\u6bd4\u4f8b\uff09<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u4ee5\u4e0b\u793a\u4f8b\u6f14\u793a\u5982\u4f55\u8ba1\u7b97 R \u4e2d\u603b\u4f53\u5e73\u5747\u503c\u548c\u603b\u4f53\u6bd4\u4f8b\u7684\u70b9\u4f30\u8ba1\u3002<\/span><\/p>\n

\u793a\u4f8b 1\uff1a\u603b\u4f53\u5e73\u5747\u503c\u7684\u70b9\u4f30\u8ba1<\/strong><\/span><\/h3>\n

\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u4f30\u8ba1\u7279\u5b9a\u7530\u5730\u4e2d\u7279\u5b9a\u7c7b\u578b\u690d\u7269\u7684\u5e73\u5747\u9ad8\u5ea6\uff08\u4ee5\u82f1\u5bf8\u4e3a\u5355\u4f4d\uff09\u3002\u6211\u4eec\u6536\u96c6\u4e86 13 \u682a\u690d\u7269\u7684\u7b80\u5355\u968f\u673a\u6837\u672c\uff0c\u5e76\u6d4b\u91cf\u4e86\u6bcf\u682a\u690d\u7269\u7684\u9ad8\u5ea6\u3002<\/span><\/p>\n

\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u8ba1\u7b97\u6837\u672c\u5747\u503c\uff1a<\/span><\/p>\n

 #define data\n<\/span>data <- c(8, 8, 9, 12, 13, 13, 14, 15, 19, 22, 23, 23, 24)\n\n#calculate sample mean\n<\/span>mean(data, na. rm<\/span> = TRUE<\/span> )\n\n[1] 15.61538<\/strong><\/pre>\n
\u6837\u672c\u5e73\u5747\u503c\u4e3a15.6<\/b>\u82f1\u5bf8\u3002\u8fd9\u4ee3\u8868\u4e86\u6211\u4eec\u5bf9\u603b\u4f53\u5e73\u5747\u503c\u7684\u70b9\u4f30\u8ba1\u3002<\/span><\/p>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u8ba1\u7b97\u603b\u4f53\u5e73\u5747\u503c\u7684 95% \u7f6e\u4fe1\u533a\u95f4\uff1a<\/span><\/p>\n
 #find sample size, sample mean, and sample standard deviation\n<\/span>n <- length(data)\nxbar <- mean(data, na. rm<\/span> = TRUE<\/span> )\ns <- sd(data)\n\n#calculate margin of error\n<\/span>margin <- qt(0.975,df=n-1)*s\/sqrt(n)\n\n#calculate lower and upper bounds of confidence interval\n<\/span>low <- xbar - margin\nlow\n\n[1] 12.03575\n\nhigh <- xbar + margin\n<\/span>high\n\n[1] 19.19502\n\n<\/strong><\/pre>\n
\u603b\u4f53\u5e73\u5747\u503c\u7684 95% \u7f6e\u4fe1\u533a\u95f4\u4e3a[12.0, 19.2]<\/strong>\u82f1\u5bf8\u3002<\/span><\/p>\n
\u793a\u4f8b2\uff1a\u4eba\u53e3\u6bd4\u4f8b\u7684\u70b9\u4f30\u8ba1<\/strong><\/span><\/h3>\n
\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u4f30\u8ba1\u67d0\u4e2a\u57ce\u5e02\u652f\u6301\u67d0\u9879\u6cd5\u5f8b\u7684\u4eba\u53e3\u6bd4\u4f8b\u3002\u6211\u4eec\u968f\u673a\u91c7\u8bbf\u4e86 20 \u540d\u516c\u6c11\u3002<\/span><\/p>\n
\u4e0b\u9762\u7684\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u8ba1\u7b97\u6837\u672c\u6bd4\u4f8b\uff1a<\/span><\/p>\n
 #define data\n<\/span>data <- c('Y', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'Y',\n          'N', 'Y', 'Y', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'N')\n\n#find total sample size\n<\/span>n <- length(data)\n\n#find number who responded 'Yes'\n<\/span>k <- sum(data == ' Y<\/span> ') \n\n#find sample proportion\n<\/span>p <- k\/n\n\np\n\n[1] 0.6\n<\/strong><\/pre>\n
\u652f\u6301\u8be5\u6cd5\u5f8b\u7684\u516c\u6c11\u6837\u672c\u6bd4\u4f8b\u4e3a0.6<\/b> \u3002\u8fd9\u4ee3\u8868\u4e86\u6211\u4eec\u5bf9\u4eba\u53e3\u6bd4\u4f8b\u7684\u70b9\u4f30\u8ba1\u3002<\/span><\/p>\n
\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u8ba1\u7b97\u603b\u4f53\u5e73\u5747\u503c\u7684 95% \u7f6e\u4fe1\u533a\u95f4\uff1a<\/span><\/p>\n
 #find total sample size\n<\/span>n <- length(data)\n\n#find number who responded 'Yes'\n<\/span>k <- sum(data == ' Y<\/span> ') \n\n#find sample proportion\n<\/span>p <- k\/n\n\n#calculate margin of error\n<\/span>margin <- qnorm(0.975)*sqrt(p*(1-p)\/n)\n\n#calculate lower and upper bounds of confidence interval\n<\/span>low <- p - margin\nlow\n\n[1] 0.3852967\n\nhigh <- p + margin\nhigh\n\n[1] 0.8147033\n<\/strong><\/pre>\n
\u4eba\u53e3\u6bd4\u4f8b\u7684 95% \u7f6e\u4fe1\u533a\u95f4\u4e3a[0.39, 0.81]<\/strong> \u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n
\u5982\u4f55\u5728R\u4e2d\u8ba1\u7b97\u4e94\u4e2a\u6570\u5b57\u7684\u603b\u548c<\/a>
\u5982\u4f55\u5728 R \u4e2d\u627e\u5230\u7f6e\u4fe1\u533a\u95f4<\/a>
\u5982\u4f55\u5728 R \u4e2d\u7ed8\u5236\u7f6e\u4fe1\u533a\u95f4<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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