{"id":2944,"date":"2023-07-19T23:21:19","date_gmt":"2023-07-19T23:21:19","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%83%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8\/"},"modified":"2023-07-19T23:21:19","modified_gmt":"2023-07-19T23:21:19","slug":"r%e1%84%8b%e1%85%b4-%e1%84%83%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%83%e1%85%a1%e1%84%8c%e1%85%ae%e1%86%bc-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8\/","title":{"rendered":"\ub2e4\uc911 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec r\uc758 \uac12\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

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\ub2e4\uc74c \uae30\ubcf8 \uad6c\ubb38\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc801\ud569 \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec R\uc758 \uac12\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/span><\/p>\n

 #define new observation\n<\/span>new <- data. frame<\/span> (x1=c(5), x2=c(10), x3=c(12.5))\n\n#use fitted model to predict the response value for the new observation\n<\/span>predict(model, newdata=new)<\/strong><\/pre>\n
 \ub2e4\uc74c \uc608\uc5d0\uc11c\ub294 \uc774 \uae30\ub2a5\uc744 \uc2e4\uc81c\ub85c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608: \uc801\ud569 \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac12 \uc608\uce21<\/strong><\/span><\/h3>\n
 \ub18d\uad6c \uc120\uc218\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub2e4\uc74c\uacfc \uac19\uc740 \ub370\uc774\ud130 \uc138\ud2b8\uac00 R\uc5d0 \uc788\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#view data frame\n<\/span>df\n\n  rating points assists rebounds\n1 67 8 4 1\n2 75 12 6 4\n3 79 16 6 3\n4 85 15 5 3\n5 90 22 3 2\n6 96 28 8 6\n7 97 24 7 7\n<\/strong><\/pre>\n
 \uc774\uc81c \ud3ec\uc778\ud2b8<\/strong> , \uc5b4\uc2dc\uc2a4\ud2b8<\/strong> , \ub9ac\ubc14\uc6b4\ub4dc\ub97c<\/strong> \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \ub4f1\uae09\uc744<\/strong> \uc751\ub2f5 \ubcc0\uc218<\/a> \ub85c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud558\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #fit multiple linear regression model\n<\/span>model <- lm(rating ~ points + assists + rebounds, data=df)\n\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
 Estimate<\/strong> \uc5f4\uc758 \uac12\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc801\ud569 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc810\uc218 = 66.4355 + 1.2151(\ud3ec\uc778\ud2b8) \u2013 2.5968(\uc5b4\uc2dc\uc2a4\ud2b8) + 2.8202(\ub9ac\ubc14\uc6b4\ub4dc)<\/span><\/p>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec 20\ub4dd\uc810, 5\uc5b4\uc2dc\uc2a4\ud2b8, 2\ub9ac\ubc14\uc6b4\ub4dc\ub97c \ubcf4\uc720\ud55c \uc0c8\ub85c\uc6b4 \uc120\uc218\uc758 \ub4f1\uae09\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #define new player\n<\/span>new <- data. frame<\/span> (points=c(20), assists=c(5), rebounds=c(2))\n\n#use the fitted model to predict the rating for the new player\n<\/span>predict(model, newdata=new)\n\n       1 \n83.39607 \n<\/strong><\/pre>\n
 \ubaa8\ub378\uc740 \uc774 \uc0c8\ub85c\uc6b4 \ud50c\ub808\uc774\uc5b4\uc758 \ud3c9\uc810\uc774 83.39607<\/strong> \uc774\ub77c\uace0 \uc608\uce21\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc0c8\ub85c\uc6b4 \ud50c\ub808\uc774\uc5b4\uc758 \uac12\uc744 \uc801\ud569\ud55c \ud68c\uadc0 \ubc29\uc815\uc2dd\uc5d0 \uc0bd\uc785\ud558\uc5ec \uc774\uac83\uc774 \uc62c\ubc14\ub978\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n