{"id":1332,"date":"2023-07-26T20:21:26","date_gmt":"2023-07-26T20:21:26","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%80%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc-%e1%84%8e%e1%85%ac%e1%84%89%e1%85%a9-%e1%84%8c%e1%85%a6%e1%84%80%e1%85%a9%e1%86%b8\/"},"modified":"2023-07-26T20:21:26","modified_gmt":"2023-07-26T20:21:26","slug":"r%e1%84%8b%e1%85%b4-%e1%84%80%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc-%e1%84%8e%e1%85%ac%e1%84%89%e1%85%a9-%e1%84%8c%e1%85%a6%e1%84%80%e1%85%a9%e1%86%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%80%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc-%e1%84%8e%e1%85%ac%e1%84%89%e1%85%a9-%e1%84%8c%e1%85%a6%e1%84%80%e1%85%a9%e1%86%b8\/","title":{"rendered":"R\uc5d0\uc11c \uac00\uc911 \ucd5c\uc18c \uc81c\uacf1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\uc120\ud615 \ud68c\uadc0 \ubd84\uc11d\uc758 \uc8fc\uc694 \uac00\uc815<\/a> \uc911 \ud558\ub098\ub294 \uc794\ucc28\uac00<\/a> \uc608\uce21 \ubcc0\uc218\uc758 \uac01 \uc218\uc900\uc5d0\uc11c \ub4f1\ubd84\uc0b0\uc73c\ub85c \ubd84\ud3ec\ub41c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774 \uac00\uc815\uc744 \ub4f1\ubd84\uc0b0\uc131(homoscedasticity)<\/strong> \uc774\ub77c\uace0 \ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \uac00\uc815\uc774 \uc874\uc911\ub418\uc9c0 \uc54a\uc73c\uba74 \uc794\ucc28\uc5d0 \uc774\ubd84\uc0b0\uc131\uc774<\/a> \uc874\uc7ac\ud55c\ub2e4\uace0 \ud569\ub2c8\ub2e4. \uc774\ub7f0 \uc77c\uc774 \ubc1c\uc0dd\ud558\uba74 \ud68c\uadc0 \uacb0\uacfc\ub97c \uc2e0\ub8b0\ud560 \uc218 \uc5c6\uac8c \ub429\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 \uac00\uc911\uce58 \ucd5c\uc18c \uc81c\uacf1 \ud68c\uadc0\ub97c<\/strong> \uc0ac\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub294 \uc624\ub958 \ubd84\uc0b0\uc774 \ub0ae\uc740 \uad00\uce21\uce58<\/a> \uac00 \uc624\ub958 \ubd84\uc0b0\uc774 \ud070 \uad00\uce21\uce58\uc5d0 \ube44\ud574 \ub354 \ub9ce\uc740 \uc815\ubcf4\ub97c \ud3ec\ud568\ud558\uae30 \ub54c\ubb38\uc5d0 \ub354 \ub9ce\uc740 \uac00\uc911\uce58\ub97c \ubc1b\ub3c4\ub85d \uad00\uce21\uce58\uc5d0 \uac00\uc911\uce58\ub97c \ud560\ub2f9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \uac00\uc911\uce58 \ucd5c\uc18c \uc81c\uacf1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n

1\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131<\/strong><\/span><\/h3>\n

\ub2e4\uc74c \ucf54\ub4dc\ub294 16\uba85\uc758 \ud559\uc0dd\uc5d0 \ub300\ud55c \ud559\uc2b5 \uc2dc\uac04\uacfc \ud574\ub2f9 \uc2dc\ud5d8 \uc810\uc218\ub97c \ud3ec\ud568\ud558\ub294 \ub370\uc774\ud130 \ud504\ub808\uc784\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/span><\/p>\n

 df <- data.frame(hours=c(1, 1, 2, 2, 2, 3, 4, 4, 4, 5, 5, 5, 6, 6, 7, 8),\n                 score=c(48, 78, 72, 70, 66, 92, 93, 75, 75, 80, 95, 97, 90, 96, 99, 99))\n<\/strong><\/pre>\n
 2\ub2e8\uacc4: \uc120\ud615 \ud68c\uadc0 \uc218\ud589<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uc73c\ub85c, lm()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2dc\uac04\uc744 \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \uc810\uc218 \ub97c \uc751\ub2f5 \ubcc0\uc218<\/a> \ub85c \uc0ac\uc6a9\ud558\ub294 \uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744<\/a> \ud53c\ud305\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #fit simple linear regression model\n<\/span>model <- lm(score ~ hours, data = df)\n\n#view summary of model\n<\/span>summary(model)\n\nCall:\nlm(formula = score ~ hours, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-17,967 -5,970 -0.719 7,531 15,032 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) 60,467 5,128 11,791 1.17e-08 ***\nhours 5,500 1,127 4,879 0.000244 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 9.224 on 14 degrees of freedom\nMultiple R-squared: 0.6296, Adjusted R-squared: 0.6032 \nF-statistic: 23.8 on 1 and 14 DF, p-value: 0.0002438\n<\/strong><\/pre>\n
 3\ub2e8\uacc4: \uc774\ubd84\uc0b0\uc131 \ud14c\uc2a4\ud2b8<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uc73c\ub85c, \uc774\ubd84\uc0b0\uc131\uc744 \uc2dc\uac01\uc801\uc73c\ub85c \ud655\uc778\ud558\uae30 \uc704\ud574 \uc794\ucc28\uc640 \uc801\ud569\uce58\uc758 \ud50c\ub86f\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.<\/span> <\/p>\n
 #create residual vs. fitted plot\n<\/span>plot( fitted<\/span> (model), resid<\/span> (model), xlab=' Fitted Values<\/span> ', ylab=' Residuals<\/span> ')\n\n#add a horizontal line at 0 \n<\/span>abline(0,0)<\/strong> <\/pre>\n
\"\"<\/p>\n
 \uadf8\ub798\ud504\uc5d0\uc11c \uc794\ucc28\uac00 “\uc6d0\ubfd4” \ubaa8\uc591\uc744 \uac16\uace0 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc794\ucc28\ub294 \uadf8\ub798\ud504 \uc804\uccb4\uc5d0 \ub4f1\ubd84\uc0b0\uc73c\ub85c \ubd84\ud3ec\ub418\uc5b4 \uc788\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774\ubd84\uc0b0\uc131\uc744 \uacf5\uc2dd\uc801\uc73c\ub85c \ud14c\uc2a4\ud2b8\ud558\uae30 \uc704\ud574 Breusch-Pagan \ud14c\uc2a4\ud2b8\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #load lmtest package\n<\/span>library<\/span> (lmtest)\n\n#perform Breusch-Pagan test<\/span>\nbptest(model)\n\n\tstudentized Breusch-Pagan test\n\ndata: model\nBP = 3.9597, df = 1, p-value = 0.0466\n<\/strong><\/pre>\n
 Breusch-Pagan \ud14c\uc2a4\ud2b8\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uadc0\ubb34 \uac00\uc124\uacfc \ub300\ub9bd<\/span> \uac00\uc124\uc744<\/a><\/span> \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n