{"id":1764,"date":"2023-07-25T02:18:14","date_gmt":"2023-07-25T02:18:14","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e8%a7%a3%e9%87%8a-prt-%e5%9b%9e%e5%bd%92%e8%be%93%e5%87%ba-r\/"},"modified":"2023-07-25T02:18:14","modified_gmt":"2023-07-25T02:18:14","slug":"%e8%a7%a3%e9%87%8a-prt-%e5%9b%9e%e5%bd%92%e8%be%93%e5%87%ba-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e8%a7%a3%e9%87%8a-prt-%e5%9b%9e%e5%bd%92%e8%be%93%e5%87%ba-r\/","title":{"rendered":"\u5982\u4f55\u89e3\u91ca r \u4e2d\u56de\u5f52\u6a21\u578b\u8f93\u51fa\u4e2d\u7684 pr(&gt;|t|)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u6bcf\u5f53\u60a8\u5728 R \u4e2d\u6267\u884c\u7ebf\u6027\u56de\u5f52\u65f6\uff0c\u56de\u5f52\u6a21\u578b\u7684\u8f93\u51fa\u5c06\u4ee5\u4ee5\u4e0b\u683c\u5f0f\u663e\u793a\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\">Coefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)  \n(Intercept) 10.0035 5.9091 1.693 0.1513  \nx1 1.4758 0.5029 2.935 0.0325 *\nx2 -0.7834 0.8014 -0.978 0.3732<\/span> \n<\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>Pr(&gt;|t|)<\/strong>\u5217\u8868\u793a\u4e0e<strong>t \u503c<\/strong>\u5217\u4e2d\u7684\u503c\u5173\u8054\u7684 p \u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5982\u679c p \u503c\u4f4e\u4e8e\u4e00\u5b9a\u7684\u663e\u7740\u6027\u6c34\u5e73\uff08\u4f8b\u5982 \u03b1 = 0.05\uff09\uff0c\u5219\u8ba4\u4e3a\u9884\u6d4b\u53d8\u91cf\u4e0e\u6a21\u578b\u4e2d\u7684\u54cd\u5e94\u53d8\u91cf\u5177\u6709\u7edf\u8ba1\u663e\u7740\u5173\u7cfb\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u663e\u793a\u5982\u4f55\u89e3\u91ca\u7ed9\u5b9a\u56de\u5f52\u6a21\u578b\u7684 Pr(&gt;|t|) \u5217\u4e2d\u7684\u503c\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1a\u5982\u4f55\u89e3\u91ca Pr(&gt;|t|) \u503c<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u4f7f\u7528\u9884\u6d4b\u53d8\u91cf<strong>x1<\/strong>\u548c<strong>x2<\/strong>\u4ee5\u53ca\u5355\u4e2a\u54cd\u5e94\u53d8\u91cf<strong>y<\/strong>\u62df\u5408<a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52-1\/\" target=\"_blank\" rel=\"noopener\">\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u521b\u5efa\u6570\u636e\u6846\u5e76\u5c06\u56de\u5f52\u6a21\u578b\u62df\u5408\u5230\u6570\u636e\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#create data frame\n<\/span>df &lt;- data. <span style=\"color: #3366ff;\">frame<\/span> (x1=c(1, 3, 3, 4, 4, 5, 6, 6),\n                 x2=c(7, 7, 5, 6, 5, 4, 5, 6),\n                 y=c(8, 8, 9, 9, 13, 14, 17, 14))\n\n<span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>model &lt;- lm(y ~ x1 + x2, data=df)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = y ~ x1 + x2, data = df)\n\nResiduals:\n      1 2 3 4 5 6 7 8 \n 2.0046 -0.9470 -1.5138 -2.2062 1.0104 -0.2488 2.0588 -0.1578 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)  \n(Intercept) 10.0035 5.9091 1.693 0.1513  \nx1 1.4758 0.5029 2.935 0.0325 *\nx2 -0.7834 0.8014 -0.978 0.3732  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 1.867 on 5 degrees of freedom\nMultiple R-squared: 0.7876, Adjusted R-squared: 0.7026 \nF-statistic: 9.268 on 2 and 5 DF, p-value: 0.0208<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca Pr(&gt;|t|) \u5217\u4e2d\u7684\u503c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u9884\u6d4b\u53d8\u91cf x1 \u7684 p \u503c\u4e3a<strong>0.0325<\/strong> \u3002\u7531\u4e8e\u8be5\u503c\u5c0f\u4e8e 0.05\uff0c\u56e0\u6b64\u4e0e\u6a21\u578b\u4e2d\u7684\u54cd\u5e94\u53d8\u91cf\u5b58\u5728\u7edf\u8ba1\u663e\u7740\u5173\u7cfb\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u9884\u6d4b\u53d8\u91cf x2 \u7684 p \u503c\u4e3a<strong>0.3732<\/strong> \u3002\u7531\u4e8e\u8be5\u503c\u4e0d\u5c0f\u4e8e0.05\uff0c\u56e0\u6b64\u4e0e\u6a21\u578b\u4e2d\u7684\u54cd\u5e94\u53d8\u91cf\u4e0d\u5177\u6709\u7edf\u8ba1\u663e\u7740\u5173\u7cfb\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7cfb\u6570\u8868\u4e0b\u65b9\u7684<a href=\"https:\/\/statorials.org\/cn\/r\u4e2d\u7684\u4ee3\u7801\u542b\u4e49\/\" target=\"_blank\" rel=\"noopener\">\u663e\u7740\u6027\u4ee3\u7801<\/a>\u544a\u8bc9\u6211\u4eec\uff0cp \u503c 0.0325 \u65c1\u8fb9\u7684\u5355\u4e2a\u661f\u53f7 (*) \u8868\u793a p \u503c\u5728 \u03b1 = 0.05 \u65f6\u5177\u6709\u7edf\u8ba1\u663e\u7740\u6027\u3002<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Pr(&gt;|t|) \u5b9e\u9645\u4e0a\u662f\u5982\u4f55\u8ba1\u7b97\u7684\uff1f<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f Pr(&gt;|t|) \u503c\u7684\u5b9e\u9645\u8ba1\u7b97\u65b9\u5f0f\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 1\uff1a\u8ba1\u7b97 t \u503c<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u4f7f\u7528\u4ee5\u4e0b\u516c\u5f0f\u8ba1\u7b97<strong>t \u503c<\/strong>\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>t \u503c<\/strong>= \u4f30\u8ba1\u503c \/ \u6807\u51c6\u503c\u9519\u8bef<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u4ee5\u4e0b\u662f\u8ba1\u7b97\u9884\u6d4b\u53d8\u91cf x1 \u7684 t \u503c\u7684\u65b9\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate t-value<\/span>\n1.4758 \/ .5029\n\n[1] 2.934579\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 2\uff1a\u8ba1\u7b97 p \u503c<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u8ba1\u7b97 p \u503c\u3002\u8fd9\u8868\u793a t \u5206\u5e03\u7684\u7edd\u5bf9\u503c\u5927\u4e8e 2.935 \u7684\u6982\u7387\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u5728R\u4e2d\u4f7f\u7528\u4ee5\u4e0b\u516c\u5f0f\u6765\u8ba1\u7b97\u8be5\u503c\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>p \u503c<\/strong>= 2 * pt\uff08abs\uff08t \u503c\uff09\uff0c\u6b8b\u5dee df\uff0clower.tail = FALSE\uff09<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u4ee5\u4e0b\u662f\u5982\u4f55\u8ba1\u7b97\u5177\u6709 5 \u4e2a\u5269\u4f59\u81ea\u7531\u5ea6\u7684 t \u503c 2.935 \u7684 p \u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#calculate p-value<\/span>\n2 * pt( <span style=\"color: #3366ff;\">abs<\/span> (2.935), 5, lower. <span style=\"color: #3366ff;\">tail<\/span> = <span style=\"color: #008000;\">FALSE<\/span> )\n\n[1] 0.0324441\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u6b64 p \u503c\u4e0e\u4e0a\u9762\u56de\u5f52\u8f93\u51fa\u4e2d\u7684 p \u503c\u5339\u914d\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f\uff1a<\/strong>\u6b8b\u5dee\u81ea\u7531\u5ea6\u7684\u503c\u4f4d\u4e8e\u56de\u5f52\u8f93\u51fa\u7684\u5e95\u90e8\u3002\u5728\u6211\u4eec\u7684\u793a\u4f8b\u4e2d\uff0c\u7ed3\u679c\u662f 5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>Residual standard error: 1.867 on <span style=\"color: #ff0000;\">5<\/span> degrees of freedom\n<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><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 \/><a href=\"https:\/\/statorials.org\/cn\/\u5728-r-\u4e2d\u7ed8\u5236\u591a\u5143\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u7ed8\u5236\u591a\u5143\u7ebf\u6027\u56de\u5f52\u7ed3\u679c<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6bcf\u5f53\u60a8\u5728 R \u4e2d\u6267\u884c\u7ebf\u6027\u56de\u5f52\u65f6\uff0c\u56de\u5f52\u6a21\u578b\u7684\u8f93\u51fa\u5c06\u4ee5\u4ee5\u4e0b\u683c\u5f0f\u663e\u793a\uff1a Coefficients: Estimate [&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-1764","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\u89e3\u91ca R \u4e2d\u56de\u5f52\u6a21\u578b\u8f93\u51fa\u4e2d\u7684 Pr(&gt;|t|)<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u793a\u4f8b\u89e3\u91ca\u5982\u4f55\u89e3\u91ca R \u56de\u5f52\u6a21\u578b\u8f93\u51fa\u4e2d\u7684 Pr(&gt;|t|) \u503c\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/\u89e3\u91ca-prt-\u56de\u5f52\u8f93\u51fa-r\/\" \/>\n<meta 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