{"id":4266,"date":"2023-07-12T10:05:09","date_gmt":"2023-07-12T10:05:09","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%87%e1%85%ae%e1%84%87%e1%85%ae%e1%86%ab-%e1%84%8c%e1%85%b5%e1%86%b8%e1%84%92%e1%85%a1%e1%86%b8-lm\/"},"modified":"2023-07-12T10:05:09","modified_gmt":"2023-07-12T10:05:09","slug":"r%e1%84%8b%e1%85%b4-%e1%84%87%e1%85%ae%e1%84%87%e1%85%ae%e1%86%ab-%e1%84%8c%e1%85%b5%e1%86%b8%e1%84%92%e1%85%a1%e1%86%b8-lm","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%87%e1%85%ae%e1%84%87%e1%85%ae%e1%86%ab-%e1%84%8c%e1%85%b5%e1%86%b8%e1%84%92%e1%85%a1%e1%86%b8-lm\/","title":{"rendered":"R\uc5d0\uc11c lm() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \ud558\uc704 \uc9d1\ud569\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

R\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\uae30 \uc704\ud574 lm()<\/a> \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \ub54c subset<\/strong> \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uba74 \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \ud558\uc704 \uc9d1\ud569\ub9cc \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

 fit <- lm(points ~ fouls + minutes, data=df, subset=(minutes><\/span> 10<\/span> ))<\/span><\/strong><\/pre>\n
 \uc774 \ud2b9\ubcc4\ud55c \uc608\ub294 \uc810\uc744<\/strong> \ubc18\uc751 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \uad70\uc911<\/strong> \uacfc \uc2dc\uac04\uc744<\/strong> \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\ub294 \ud68c\uadc0 \ubaa8\ub378\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ud558\uc704<\/strong> \uc9d1\ud569 \uc778\uc218\ub294 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud560 \ub54c \ubd84<\/strong> \ubcc0\uc218\uac00 10\ubcf4\ub2e4 \ud070 \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \ud589\ub9cc \uc0ac\uc6a9\ud574\uc57c \ud568\uc744 \uc9c0\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\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\uc5d0\uc11c lm()\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \ud504\ub808\uc784 \ud558\uc704 \uc9d1\ud569\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h2>\n
 10\uba85\uc758 \ub18d\uad6c \uc120\uc218\uac00 \ud50c\ub808\uc774\ud55c \uc2dc\uac04, \ucd1d \ud30c\uc6b8, \ucd1d \ub4dd\uc810\uc5d0 \ub300\ud55c \uc815\ubcf4\ub97c \ud3ec\ud568\ud558\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub370\uc774\ud130 \ud504\ub808\uc784\uc774 R\uc5d0 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #create data frame\n<\/span>df <- data. frame<\/span> (minutes=c(5, 10, 13, 14, 20, 22, 26, 34, 38, 40),\n                 fouls=c(5, 5, 3, 4, 2, 1, 3, 2, 1, 1),\n                 points=c(6, 8, 8, 7, 14, 10, 22, 24, 28, 30))\n\n#view data frame\n<\/span>df\n\n   minutes fouls points\n1 5 5 6\n2 10 5 8\n3 13 3 8\n4 14 4 7\n5 20 2 14\n6 22 1 10\n7 26 3 22\n8 34 2 24\n9 38 1 28\n10 40 1 30<\/strong><\/pre>\n
 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud654\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ud3ec\uc778\ud2b8 = \u03b2 0<\/sub> + \u03b2 1<\/sub> (\ubd84) + \u03b2 2<\/sub> (\ud30c\uc6b8)<\/strong><\/span><\/p>\n
 \uadf8\ub7ec\ub098 \ubd84<\/strong> \ubcc0\uc218\uac00 10\ubcf4\ub2e4 \ud070 \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \ud589\ub9cc \uc0ac\uc6a9\ud55c\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \ud68c\uadc0 \ubaa8\ub378\uc5d0 \ub9de\uac8c \ud558\uc704 \uc9d1\ud569<\/strong> \uc778\uc218\uc640 \ud568\uaed8 lm()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #fit multiple linear regression model (only for rows where minutes>10)\n<\/span><\/strong>fit <- lm(points ~ fouls + minutes, data=df, subset=(minutes><\/span> 10<\/span> ))\n\n#view model summary<\/span>\nsummary(fit)\n\nCall:\nlm(formula = points ~ fouls + minutes, data = df, subset = (minutes > \n    10))\n\nResiduals:\n      3 4 5 6 7 8 9 10 \n 1.2824 -2.5882 2.2000 -1.9118 2.3588 -1.7176 0.1824 0.1941 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -11.8353 4.9696 -2.382 0.063046 .  \nfouls 1.8765 1.0791 1.739 0.142536    \nminutes 0.9941 0.1159 8.575 0.000356 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 2.255 on 5 degrees of freedom\nMultiple R-squared: 0.9574, Adjusted R-squared: 0.9404 \nF-statistic: 56.19 on 2 and 5 DF, p-value: 0.0003744\n<\/span><\/strong><\/pre>\n
 nobs()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\uae30 \uc704\ud574 \uc2e4\uc81c\ub85c \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \uad00\uce21\uac12\uc774 \uc5bc\ub9c8\ub098 \ub9ce\uc774 \uc0ac\uc6a9\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #view number of observations used to fit model\n<\/span>nobs(fit)\n\n[1] 8\n<\/span><\/span><\/strong><\/pre>\n
 \ubaa8\ub378\uc744 \uc801\ud569\uc2dc\ud0a4\uae30 \uc704\ud574 \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 8<\/strong> \uac1c \ud589\uc774 \uc0ac\uc6a9\ub418\uc5c8\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc6d0\ubcf8 \ub370\uc774\ud130 \ud504\ub808\uc784\uc744 \ubcf4\uba74 \uc815\ud655\ud788 8\uac1c\uc758<\/strong> \ud589\uc774 \ubd84<\/strong> \ubcc0\uc218\uc5d0 \ub300\ud574 10\ubcf4\ub2e4 \ud070 \uac12\uc744 \uac16\uace0 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud560 \ub54c \ud574\ub2f9 \ud589\ub9cc \uc0ac\uc6a9\ub418\uc5c8\uc74c\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \ub610\ud55c \ud558\uc704 \uc9d1\ud569<\/strong> \uc778\uc218\uc5d0 &<\/strong> \uc5f0\uc0b0\uc790\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec \uc870\uac74\uc5d0 \ub530\ub77c \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \ud558\uc704 \uc9d1\ud569\uc744 \uc9c0\uc815\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4 \ub2e4\uc74c \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubd84\uc774<\/strong> 10\ubcf4\ub2e4 \ud06c\uace0 \uc624\ub958\uac00<\/strong> 4\ubcf4\ub2e4 \uc791\uc740 \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \ud589\ub9cc \uc0ac\uc6a9\ud558\uc5ec \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd9c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #fit multiple linear regression model (only where minutes>10 & fouls<4)\n<\/span><\/strong>fit <- lm(points ~ fouls + minutes, data=df, subset=(minutes><\/span> 10 &<\/span> fouls< 4<\/span><\/span><\/span> ))\n\n#view number of observations used to fit model<\/span>\nnobs(fit)\n\n[1] 7\n<\/span><\/strong><\/pre>\n
 \uacb0\uacfc\uc5d0\uc11c \uc6b0\ub9ac\ub294 \uc774 \ud2b9\uc815 \ubaa8\ub378\uc744 \ub9de\ucd94\uae30 \uc704\ud574 \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 7<\/strong> \uac1c \ud589\uc774 \uc0ac\uc6a9\ub418\uc5c8\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h2>\n
 \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
 R\uc5d0\uc11c \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 R\uc5d0\uc11c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95<\/a>
 R\uc5d0\uc11c \uc794\ucc28 \ud50c\ub86f\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
R\uc5d0\uc11c \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\uae30 \uc704\ud574 lm() \ud568\uc218\ub97c \uc0ac\uc6a9\ud560 \ub54c subset \uc778\uc218\ub97c \uc0ac\uc6a9\ud558\uba74 \ub370\uc774\ud130 \ud504\ub808\uc784\uc758 \ud558\uc704 \uc9d1\ud569\ub9cc \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. fit <- lm(points ~ fouls + minutes, data=df, subset=(minutes> 10 )) \uc774 \ud2b9\ubcc4\ud55c \uc608\ub294 \uc810\uc744 \ubc18\uc751 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \uad70\uc911 \uacfc \uc2dc\uac04\uc744 \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\ub294 \ud68c\uadc0 \ubaa8\ub378\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4. \ud558\uc704 \uc9d1\ud569 \uc778\uc218\ub294 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud560 \ub54c \ubd84 \ubcc0\uc218\uac00 […]<\/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-4266","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nR\uc758 lm() \ud568\uc218\uc640 \ud568\uaed8 \ub370\uc774\ud130 \ud504\ub808\uc784 \ud558\uc704 \uc9d1\ud569\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95 - 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