{"id":3620,"date":"2023-07-16T12:25:52","date_gmt":"2023-07-16T12:25:52","guid":{"rendered":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e5%9b%9e%e5%bd%92%e7%b3%bb%e6%95%b0%e7%9a%84%e7%bd%ae%e4%bf%a1%e5%8c%ba%e9%97%b4\/"},"modified":"2023-07-16T12:25:52","modified_gmt":"2023-07-16T12:25:52","slug":"r-%e4%b8%ad%e5%9b%9e%e5%bd%92%e7%b3%bb%e6%95%b0%e7%9a%84%e7%bd%ae%e4%bf%a1%e5%8c%ba%e9%97%b4","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r-%e4%b8%ad%e5%9b%9e%e5%bd%92%e7%b3%bb%e6%95%b0%e7%9a%84%e7%bd%ae%e4%bf%a1%e5%8c%ba%e9%97%b4\/","title":{"rendered":"\u5982\u4f55\u8ba1\u7b97r\u4e2d\u56de\u5f52\u7cfb\u6570\u7684\u7f6e\u4fe1\u533a\u95f4"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u5728\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u4e2d\uff0c\u56de\u5f52\u7cfb\u6570\u544a\u8bc9\u6211\u4eec\u4e0e\u9884\u6d4b\u53d8\u91cf\u589e\u52a0\u4e00\u4e2a\u5355\u4f4d\u76f8\u5173\u7684<a href=\"https:\/\/statorials.org\/cn\/\u53d8\u91cf\u89e3\u91ca\u6027\u53cd\u5e94\/\" target=\"_blank\" rel=\"noopener\">\u54cd\u5e94\u53d8\u91cf<\/a>\u7684\u5e73\u5747\u53d8\u5316\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u516c\u5f0f\u6765\u8ba1\u7b97\u56de\u5f52\u7cfb\u6570\u7684\u7f6e\u4fe1\u533a\u95f4\uff1a<\/span><\/p>\n<blockquote>\n<p> <strong><span style=\"color: #000000;\">\u03b2 <sub>1<\/sub>\u7684\u7f6e\u4fe1\u533a\u95f4\uff1ab <sub>1<\/sub> \u00b1 t <sub>1-\u03b1\/2, n-2<\/sub> * se(b <sub>1<\/sub> )<\/span><\/strong><\/p>\n<\/blockquote>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span> <\/p>\n<ul>\n<li><strong>&nbsp;<\/strong> <span style=\"color: #000000;\"><strong>b <sub>1<\/sub><\/strong> = \u56de\u5f52\u8868\u4e2d\u663e\u793a\u7684\u56de\u5f52\u7cfb\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>t <sub>1-\u221d\/2, n-2<\/sub><\/strong> = \u5177\u6709 n-2 \u81ea\u7531\u5ea6\u7684 1-\u221d \u7f6e\u4fe1\u6c34\u5e73\u7684\u4e34\u754c t \u503c\uff0c\u5176\u4e2d<em>n<\/em>\u662f\u6570\u636e\u96c6\u4e2d\u7684\u89c2\u6d4b\u603b\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>se(b <sub>1<\/sub> )<\/strong> = \u56de\u5f52\u8868\u4e2d\u663e\u793a\u7684 b <sub>1<\/sub>\u6807\u51c6\u8bef\u5dee<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u8ba1\u7b97\u56de\u5f52\u659c\u7387\u7684\u7f6e\u4fe1\u533a\u95f4\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aR \u4e2d\u56de\u5f52\u7cfb\u6570\u7684\u7f6e\u4fe1\u533a\u95f4<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u62df\u5408\u4e00\u4e2a\u7b80\u5355\u7684\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u4f7f\u7528<strong>\u5b66\u4e60\u65f6\u95f4<\/strong>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c<strong>\u8003\u8bd5\u6210\u7ee9<\/strong>\u4f5c\u4e3a\u7279\u5b9a\u73ed\u7ea7 15 \u540d\u5b66\u751f\u7684\u54cd\u5e94\u53d8\u91cf\uff1a<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-22174 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/intercepter1.png\" alt=\"\" width=\"190\" height=\"377\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/r\u4e2d\u7684lm\u51fd\u6570\/\" target=\"_blank\" rel=\"noopener\">lm()<\/a>\u51fd\u6570\u6765\u62df\u5408 R \u4e2d\u7684\u8fd9\u4e2a\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (hours=c(1, 2, 4, 5, 5, 6, 6, 7, 8, 10, 11, 11, 12, 12, 14),\n                 score=c(64, 66, 76, 73, 74, 81, 83, 82, 80, 88, 84, 82, 91, 93, 89))\n\n<span style=\"color: #008080;\">#fit linear regression model\n<\/span>fit &lt;- lm(score ~ hours, data=df)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(fit)\n\nCall:\nlm(formula = score ~ hours, data = df)\n\nResiduals:\n   Min 1Q Median 3Q Max \n-5,140 -3,219 -1,193 2,816 5,772 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)    \n(Intercept) 65,334 2,106 31,023 1.41e-13 ***\nhours 1.982 0.248 7.995 2.25e-06 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.641 on 13 degrees of freedom\nMultiple R-squared: 0.831, Adjusted R-squared: 0.818 \nF-statistic: 63.91 on 1 and 13 DF, p-value: 2.253e-06<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4f7f\u7528\u7ed3\u679c\u4e2d\u7684\u7cfb\u6570\u4f30\u8ba1\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u62df\u5408\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5206\u6570 = 65.334 + 1.982*\uff08\u5b66\u4e60\u65f6\u95f4\uff09<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u5c0f\u65f6\u6570\u7684\u56de\u5f52\u7cfb\u6570\u4e3a<strong>1.982<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u544a\u8bc9\u6211\u4eec\uff0c\u6bcf\u591a\u82b1\u4e00\u5c0f\u65f6\u7684\u5b66\u4e60\u65f6\u95f4\uff0c\u8003\u8bd5\u6210\u7ee9\u5c31\u4f1a\u5e73\u5747\u63d0\u9ad8<strong>1,982<\/strong>\u5206\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>confint()<\/strong>\u51fd\u6570\u8ba1\u7b97\u56de\u5f52\u7cfb\u6570\u7684 95% \u7f6e\u4fe1\u533a\u95f4\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate confidence interval for regression coefficient for 'hours'<\/span>\nconfint(fit, ' <span style=\"color: #ff0000;\">hours<\/span> ', level= <span style=\"color: #008000;\">0.95<\/span> )\n\n         2.5% 97.5%\nhours 1.446682 2.518068\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u56de\u5f52\u7cfb\u6570\u7684 95% \u7f6e\u4fe1\u533a\u95f4\u4e3a<strong>[1.446, 2.518]<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7531\u4e8e\u6b64\u7f6e\u4fe1\u533a\u95f4\u4e0d\u5305\u542b\u503c 0\uff0c\u56e0\u6b64\u6211\u4eec\u53ef\u4ee5\u5f97\u51fa\u7ed3\u8bba\uff0c\u5b66\u4e60\u65f6\u95f4\u548c\u8003\u8bd5\u6210\u7ee9\u4e4b\u95f4\u5b58\u5728\u7edf\u8ba1\u4e0a\u663e\u7740\u7684\u5173\u8054\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u901a\u8fc7\u624b\u52a8\u8ba1\u7b97\u56de\u5f52\u7cfb\u6570\u7684 95% \u7f6e\u4fe1\u533a\u95f4\u6765\u786e\u8ba4\u8fd9\u662f\u6b63\u786e\u7684\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u03b2 <sub>1<\/sub>\u7684 95% CI\uff1ab <sub>1<\/sub> \u00b1 t <sub>1-\u03b1\/2\uff0cn-2<\/sub> * se(b <sub>1<\/sub> )<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u03b2 <sub>1<\/sub>\u7684 95% CI\uff1a1.982 \u00b1 t <sub>0.975, 15-2<\/sub> * 0.248<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u03b2 <sub>1<\/sub>\u7684 95% CI\uff1a1.982 \u00b1 2.1604 * 0.248<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u03b2 <sub>1<\/sub>\u7684 95% CI\uff1a[1.446, 2.518]<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u56de\u5f52\u7cfb\u6570\u7684 95% \u7f6e\u4fe1\u533a\u95f4\u4e3a<strong>[1.446, 2.518]<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f#1<\/strong> \uff1a\u6211\u4eec\u4f7f\u7528\u9006 t \u5206\u5e03\u8ba1\u7b97\u5668\u6765\u67e5\u627e\u4e34\u754c t \u503c\uff0c\u8be5\u503c\u5bf9\u5e94\u4e8e 13 \u4e2a\u81ea\u7531\u5ea6\u7684 95% \u7f6e\u4fe1\u6c34\u5e73\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f#2<\/strong> \uff1a\u8981\u8ba1\u7b97\u5177\u6709\u4e0d\u540c\u7f6e\u4fe1\u6c34\u5e73\u7684\u7f6e\u4fe1\u533a\u95f4\uff0c\u53ea\u9700\u66f4\u6539<strong>confint()<\/strong>\u51fd\u6570\u4e2d<strong>level<\/strong>\u53c2\u6570\u7684\u503c\u5373\u53ef\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u63d0\u4f9b\u4e86\u6709\u5173 R \u4e2d\u7ebf\u6027\u56de\u5f52\u7684\u66f4\u591a\u4fe1\u606f\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u89e3\u91ca-r-\u4e2d\u7684\u56de\u5f52\u8f93\u51fa\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u89e3\u91ca R \u4e2d\u7684\u56de\u5f52\u8f93\u51fa<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/r-\u4e2d\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a><br \/>\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u903b\u8f91\u56de\u5f52<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5728\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u4e2d\uff0c\u56de\u5f52\u7cfb\u6570\u544a\u8bc9\u6211\u4eec\u4e0e\u9884\u6d4b\u53d8\u91cf\u589e\u52a0\u4e00\u4e2a\u5355\u4f4d\u76f8\u5173\u7684\u54cd\u5e94\u53d8\u91cf\u7684\u5e73\u5747\u53d8\u5316\u3002 \u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u516c\u5f0f\u6765\u8ba1\u7b97 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3620","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u8ba1\u7b97 R \u4e2d\u56de\u5f52\u7cfb\u6570\u7684\u7f6e\u4fe1\u533a\u95f4 - Statorials<\/title>\n<meta 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