{"id":3908,"date":"2023-07-14T20:16:38","date_gmt":"2023-07-14T20:16:38","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e6%9c%80%e5%b0%8f%e4%ba%8c%e4%b9%98%e6%b3%95\/"},"modified":"2023-07-14T20:16:38","modified_gmt":"2023-07-14T20:16:38","slug":"r%e4%b8%ad%e7%9a%84%e6%9c%80%e5%b0%8f%e4%ba%8c%e4%b9%98%e6%b3%95","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84%e6%9c%80%e5%b0%8f%e4%ba%8c%e4%b9%98%e6%b3%95\/","title":{"rendered":"\u5982\u4f55\u5728r\u4e2d\u4f7f\u7528\u6700\u5c0f\u4e8c\u4e58\u6cd5"},"content":{"rendered":"<hr>\n<p><span style=\"color: #000000;\"><strong>\u6700\u5c0f\u4e8c\u4e58\u6cd5<\/strong>\u662f\u6211\u4eec\u53ef\u4ee5\u7528\u6765\u627e\u5230\u6700\u9002\u5408\u7ed9\u5b9a\u6570\u636e\u96c6\u7684\u56de\u5f52\u7ebf\u7684\u65b9\u6cd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8981\u4f7f\u7528\u6700\u5c0f\u4e8c\u4e58\u6cd5\u6765\u62df\u5408 R \u4e2d\u7684\u56de\u5f52\u7ebf\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>lm()<\/strong>\u51fd\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>model &lt;- lm(response ~ predictor, data=df)\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 R \u4e2d\u4f7f\u7528\u6b64\u51fd\u6570\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aR \u4e2d\u7684\u6700\u5c0f\u4e8c\u4e58\u6cd5<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u5728 R \u4e2d\u6709\u4ee5\u4e0b\u6570\u636e\u6846\uff0c\u663e\u793a\u73ed\u7ea7 15 \u540d\u5b66\u751f\u7684\u5b66\u4e60\u5c0f\u65f6\u6570\u548c\u76f8\u5e94\u7684\u8003\u8bd5\u6210\u7ee9\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;\">#view first six rows of data frame\n<\/span>head(df)\n\n  hours score\n1 1 64\n2 2 66\n3 4 76\n4 5 73\n5 5 74\n6 6 81\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>lm()<\/strong>\u51fd\u6570\u4f7f\u7528\u6700\u5c0f\u4e8c\u4e58\u6cd5\u6765\u62df\u5408\u8be5\u6570\u636e\u7684\u56de\u5f52\u7ebf\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#use method of least squares to fit regression line\n<\/span>model &lt;- lm(score ~ hours, data=df)\n\n<span style=\"color: #008080;\">#view regression model summary<\/span>\nsummary(model)\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\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6839\u636e<strong>\u4f30\u8ba1<\/strong>\u7ed3\u679c\u5217\u4e2d\u7684\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u4ee5\u4e0b\u62df\u5408\u56de\u5f52\u7ebf\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8003\u8bd5\u6210\u7ee9 = 65.334 + 1.982\uff08\u5c0f\u65f6\uff09<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u6a21\u578b\u4e2d\u7684\u6bcf\u4e2a\u7cfb\u6570\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u622a\u53d6<\/strong>\uff1a \u5bf9\u4e8e\u5b66\u4e60 0 \u5c0f\u65f6\u7684\u5b66\u751f\uff0c\u9884\u671f\u8003\u8bd5\u6210\u7ee9\u4e3a<strong>65.334<\/strong> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u5c0f\u65f6<\/strong>\uff1a\u6bcf\u591a\u5b66\u4e60\u4e00\u5c0f\u65f6\uff0c\u9884\u671f\u8003\u8bd5\u6210\u7ee9\u5c31\u4f1a\u589e\u52a0<strong>1,982 \u5206<\/strong>\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e2a\u65b9\u7a0b\u6765\u4f30\u8ba1\u5b66\u751f\u6839\u636e\u5b66\u4e60\u65f6\u95f4\u83b7\u5f97\u7684\u8003\u8bd5\u6210\u7ee9\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u5982\u679c\u4e00\u4e2a\u5b66\u751f\u5b66\u4e60\u4e86 5 \u4e2a\u5c0f\u65f6\uff0c\u6211\u4eec\u4f30\u8ba1\u4ed6\u7684\u8003\u8bd5\u6210\u7ee9\u5c06\u4e3a 75.244\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8003\u8bd5\u5206\u6570 = 65.334 + 1.982(5) = 75.244<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u539f\u59cb\u6570\u636e\u7684\u6563\u70b9\u56fe\uff0c\u5e76\u5c06\u62df\u5408\u56de\u5f52\u7ebf\u53e0\u52a0\u5728\u56fe\u4e0a\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create scatter plot of data\n<\/span>plot(df$hours, df$score, pch=16, col=' <span style=\"color: #ff0000;\">steelblue<\/span> ')\n\n<span style=\"color: #008080;\">#add fitted regression line to scatter plot\n<\/span>abline(model)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-31432 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/methode1.jpg\" sizes=\"auto, \" srcset=\"\" alt=\"\" width=\"494\" height=\"453\"><\/p>\n<p><span style=\"color: #000000;\">\u84dd\u8272\u5706\u5708\u4ee3\u8868\u6570\u636e\uff0c\u9ed1\u7ebf\u4ee3\u8868\u62df\u5408\u56de\u5f52\u7ebf\u3002<\/span><\/p>\n<h2><strong><span style=\"color: #000000;\">\u5176\u4ed6\u8d44\u6e90<\/span><\/strong><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u6b8b\u7559\u75d5\u91cf\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u521b\u5efa\u6b8b\u5dee\u56fe<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/r-\u4e2d\u7684\u591a\u91cd\u5171\u7ebf\u6027\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 R \u4e2d\u6d4b\u8bd5\u591a\u91cd\u5171\u7ebf\u6027<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/r\u66f2\u7ebf\u8c03\u6574\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728R\u4e2d\u8fdb\u884c\u66f2\u7ebf\u62df\u5408<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u6700\u5c0f\u4e8c\u4e58\u6cd5\u662f\u6211\u4eec\u53ef\u4ee5\u7528\u6765\u627e\u5230\u6700\u9002\u5408\u7ed9\u5b9a\u6570\u636e\u96c6\u7684\u56de\u5f52\u7ebf\u7684\u65b9\u6cd5\u3002 \u8981\u4f7f\u7528\u6700\u5c0f\u4e8c\u4e58\u6cd5\u6765\u62df\u5408 R \u4e2d\u7684\u56de\u5f52\u7ebf\uff0c\u6211\u4eec\u53ef\u4ee5 [&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-3908","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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