{"id":3625,"date":"2023-07-16T11:24:36","date_gmt":"2023-07-16T11:24:36","guid":{"rendered":"https:\/\/statorials.org\/ko\/lm%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-r-%e1%84%8c%e1%85%a6%e1%84%80%e1%85%a9%e1%86%b8%e1%84%8b%e1%85%b3%e1%86%af-r%e1%84%85%e1%85%a9-%e1%84%8e%e1%85%ae%e1%84%8e%e1%85%ae%e1%86%af\/"},"modified":"2023-07-16T11:24:36","modified_gmt":"2023-07-16T11:24:36","slug":"lm%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-r-%e1%84%8c%e1%85%a6%e1%84%80%e1%85%a9%e1%86%b8%e1%84%8b%e1%85%b3%e1%86%af-r%e1%84%85%e1%85%a9-%e1%84%8e%e1%85%ae%e1%84%8e%e1%85%ae%e1%86%af","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/lm%e1%84%8b%e1%85%a6%e1%84%89%e1%85%a5-r-%e1%84%8c%e1%85%a6%e1%84%80%e1%85%a9%e1%86%b8%e1%84%8b%e1%85%b3%e1%86%af-r%e1%84%85%e1%85%a9-%e1%84%8e%e1%85%ae%e1%84%8e%e1%85%ae%e1%86%af\/","title":{"rendered":"R\uc758 lm() \ud568\uc218\uc5d0\uc11c r-\uc81c\uacf1\uc744 \ucd94\ucd9c\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec R\uc758 lm()<\/a> \ud568\uc218\uc5d0\uc11c R \uc81c\uacf1<\/a> \ubc0f \uc870\uc815\ub41c R \uc81c\uacf1<\/a> \uac12\uc744 \ucd94\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 #extract R-squared\nsummary(model)$adj.r.squared<\/span>\n\n#extract adjusted R-squared<\/span>\nsummary(model)$adj.r.squared\n<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc2e4\uc81c\ub85c \uc774 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: R\uc758 lm()\uc5d0\uc11c R-\uc81c\uacf1 \ucd94\ucd9c<\/strong><\/span><\/h2>\n
 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
 #create data frame\n<\/span>df <- data. 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#fit multiple linear regression model\n<\/span>model <- lm(rating ~ points + assists + rebounds, data=df)\n<\/strong><\/pre>\n
 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
 #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(>|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
 \ucd9c\ub825 \ud558\ub2e8 \uadfc\ucc98\uc5d0 \ubaa8\ub378\uc758 R-\uc81c\uacf1 \ubc0f \uc870\uc815\ub41c R-\uc81c\uacf1 \uac12\uc744 \uae30\ub85d\ud574 \ub450\uc138\uc694.<\/span><\/p>\n