{"id":3626,"date":"2023-07-16T11:14:31","date_gmt":"2023-07-16T11:14:31","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-lm%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%91%e1%85%ad%e1%84%8c%e1%85%ae%e1%86%ab-%e1%84%8b%e1%85%a9%e1%84%85%e1%85%b2%e1%84%85%e1%85%b3%e1%86%af-%e1%84%8e%e1%85%ae%e1%84%8e\/"},"modified":"2023-07-16T11:14:31","modified_gmt":"2023-07-16T11:14:31","slug":"r%e1%84%8b%e1%85%b4-lm%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%91%e1%85%ad%e1%84%8c%e1%85%ae%e1%86%ab-%e1%84%8b%e1%85%a9%e1%84%85%e1%85%b2%e1%84%85%e1%85%b3%e1%86%af-%e1%84%8e%e1%85%ae%e1%84%8e","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-lm%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-%e1%84%91%e1%85%ad%e1%84%8c%e1%85%ae%e1%86%ab-%e1%84%8b%e1%85%a9%e1%84%85%e1%85%b2%e1%84%85%e1%85%b3%e1%86%af-%e1%84%8e%e1%85%ae%e1%84%8e\/","title":{"rendered":"R\uc758 lm() \ud568\uc218\uc5d0\uc11c \ud45c\uc900 \uc624\ub958\ub97c \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec R\uc5d0\uc11c <a href=\"https:\/\/statorials.org\/ko\/r\u110b\u1174-lm-\u1112\u1161\u11b7\u1109\u116e\/\" target=\"_blank\" rel=\"noopener\">lm()<\/a> \ud568\uc218\uc758 \uac1c\ubcc4 \ud68c\uadc0 \uacc4\uc218\uc758 \ud45c\uc900 \uc624\ucc28\uc640 \uc794\uc5ec \ud45c\uc900 \uc624\ucc28\ub97c \ucd94\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\ubc29\ubc95 1: \uc794\uc5ec \ud45c\uc900 \uc624\ucc28 \ucd94\ucd9c<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#extract residual standard error of regression model\n<span style=\"color: #000000;\">summary(model)$sigma<\/span><\/span>\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>\ubc29\ubc95 2: \uac1c\ubcc4 \ud68c\uadc0\uacc4\uc218\uc758 \ud45c\uc900\uc624\ucc28 \ucd94\ucd9c<\/strong><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#extract standard error of individual regression coefficients\n<span style=\"color: #000000;\">sqrt(diag(vcov(model)))<\/span>\n<\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uac01 \ubc29\ubc95\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\uc608: R\uc758 lm()\uc5d0\uc11c \ud45c\uc900 \uc624\ub958 \ucd94\ucd9c<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">R\uc5d0 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud558\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/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> (rating=c(67, 75, 79, 85, 90, 96, 97),\n                 points=c(8, 12, 16, 15, 22, 28, 24),\n                 assists=c(4, 6, 6, 5, 3, 8, 7),\n                 rebounds=c(1, 4, 3, 3, 2, 6, 7))\n\n<span style=\"color: #008080;\">#fit multiple linear regression model\n<\/span>model &lt;- lm(rating ~ points + assists + rebounds, data=df)\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>summary()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud68c\uadc0 \ubaa8\ub378\uc758 \uc804\uccb4 \uc694\uc57d\uc744 \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view model summary\n<\/span>summary(model)\n\nCall:\nlm(formula = rating ~ points + assists + rebounds, data = df)\n\nResiduals:\n      1 2 3 4 5 6 7 \n-1.5902 -1.7181 0.2413 4.8597 -1.0201 -0.6082 -0.1644 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)   \n(Intercept) 66.4355 6.6932 9.926 0.00218 **\npoints 1.2152 0.2788 4.359 0.02232 * \nassists -2.5968 1.6263 -1.597 0.20860   \nrebounds 2.8202 1.6118 1.750 0.17847   \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.193 on 3 degrees of freedom\nMultiple R-squared: 0.9589, Adjusted R-squared: 0.9179 \nF-statistic: 23.35 on 3 and 3 DF, p-value: 0.01396\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\ubaa8\ud615\uc758 \uc794\ucc28 \ud45c\uc900\uc624\ucc28\ub294 3.193\uc774\uba70, \uac1c\ubcc4 \ud68c\uadc0\uacc4\uc218\uc5d0 \ub300\ud55c \uac01 \ud45c\uc900\uc624\ucc28\ub294 <strong>Std.<\/strong> \ucd9c\ub825 <strong>\uc624\ub958<\/strong> \uc5f4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ubaa8\ub378\uc5d0\uc11c \uc794\uc5ec \ud45c\uc900 \uc624\ucc28\ub9cc \ucd94\ucd9c\ud558\ub824\uba74 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#extract residual standard error of regression model<\/span>\nsummary(model)$sigma\n\n[1] 3.19339<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uadf8\ub9ac\uace0 \uac01 \uac1c\ubcc4 \ud68c\uadc0 \uacc4\uc218\uc5d0 \ub300\ud55c \ud45c\uc900 \uc624\ub958\ub9cc \ucd94\ucd9c\ud558\ub824\uba74 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#extract standard error of individual regression coefficients<\/span>\nsqrt(diag(vcov(model)))\n\n(Intercept) points assists rebounds \n  6.6931808 0.2787838 1.6262899 1.6117911 \n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc774 \uac12\uc740 \uc774\uc804\uc5d0 \ud68c\uadc0 \uacb0\uacfc\uc758 \uc804\uccb4 \uc694\uc57d\uc5d0\uc11c \ubcf8 \uac12\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uad00\ub828 \ud56d\ubaa9:<\/strong> <a href=\"https:\/\/statorials.org\/ko\/\u110c\u1161\u11ab\u110e\u1161-\u1111\u116d\u110c\u116e\u11ab-\u110b\u1169\u110e\u1161\u1105\u1173\u11af-\u1112\u1162\u1109\u1165\u11a8\u1112\u1161\u1102\u1173\u11ab-\u1107\u1161\u11bc\u1107\u1165\u11b8\/\" target=\"_blank\" rel=\"noopener\">\uc794\ucc28 \ud45c\uc900 \uc624\ub958\ub97c \ud574\uc11d\ud558\ub294 \ubc29\ubc95<\/a><\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>\ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \ub2e4\ub978 \uc77c\ubc18\uc801\uc778 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ko\/r\u110b\u1174-\u1103\u1161\u11ab\u1109\u116e\u11ab-\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1112\u116c\u1100\u1171\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u1103\u1161\u110c\u116e\u11bc-\u1109\u1165\u11ab\u1112\u1167\u11bc-\u1112\u116c\u1100\u1171-r\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a><br \/> <a href=\"https:\/\/statorials.org\/ko\/\u110c\u1161\u11ab\u110b\u1167-\u110e\u116e\u110c\u1165\u11a8-r\/\" target=\"_blank\" rel=\"noopener\">R\uc5d0\uc11c \uc794\ucc28 \ud50c\ub86f\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub2e4\uc74c \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec R\uc5d0\uc11c lm() \ud568\uc218\uc758 \uac1c\ubcc4 \ud68c\uadc0 \uacc4\uc218\uc758 \ud45c\uc900 \uc624\ucc28\uc640 \uc794\uc5ec \ud45c\uc900 \uc624\ucc28\ub97c \ucd94\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubc29\ubc95 1: \uc794\uc5ec \ud45c\uc900 \uc624\ucc28 \ucd94\ucd9c #extract residual standard error of regression model summary(model)$sigma \ubc29\ubc95 2: \uac1c\ubcc4 \ud68c\uadc0\uacc4\uc218\uc758 \ud45c\uc900\uc624\ucc28 \ucd94\ucd9c #extract standard error of individual regression coefficients sqrt(diag(vcov(model))) \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uac01 \ubc29\ubc95\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4. \uc608: R\uc758 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-3626","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the 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