{"id":781,"date":"2023-07-28T18:44:38","date_gmt":"2023-07-28T18:44:38","guid":{"rendered":"https:\/\/statorials.org\/ko\/2%e1%84%8e%e1%85%a1-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-r\/"},"modified":"2023-07-28T18:44:38","modified_gmt":"2023-07-28T18:44:38","slug":"2%e1%84%8e%e1%85%a1-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/2%e1%84%8e%e1%85%a1-%e1%84%92%e1%85%ac%e1%84%80%e1%85%b1-r\/","title":{"rendered":"R\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\ub450 \ubcc0\uc218\uac00 \uc120\ud615 \uad00\uacc4\ub97c \uac16\ub294 \uacbd\uc6b0 \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\ub97c<\/a> \uc0ac\uc6a9\ud558\uc5ec \uad00\uacc4\ub97c \uc815\ub7c9\ud654\ud560 \uc218 \uc788\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n

\"\uc120\ud615<\/p>\n

\uadf8\ub7ec\ub098 \ub450 \ubcc0\uc218\uac00 2\ucc28 \uad00\uacc4\ub97c \uac16\ub294 \uacbd\uc6b0 2\ucc28 \ud68c\uadc0\ub97c<\/strong> \uc0ac\uc6a9\ud558\uc5ec \uad00\uacc4\ub97c \uc815\ub7c9\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n

\"2\ucc28<\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc608: R\uc758 2\ucc28 \ud68c\uadc0<\/strong><\/h3>\n

\uadfc\ubb34 \uc2dc\uac04\uacfc \ubcf4\uace0\ub41c \ud589\ubcf5 \uc0ac\uc774\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\uace0 \uc2f6\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 11\uba85\uc758 \uc11c\ub85c \ub2e4\ub978 \uc0ac\ub78c\ub4e4\uc5d0 \ub300\ud55c \uc8fc\ub2f9 \uadfc\ubb34 \uc2dc\uac04\uacfc \ubcf4\uace0\ub41c \ud589\ubcf5 \uc218\uc900(0~100\uc810)\uc5d0 \ub300\ud55c \ub2e4\uc74c\uacfc \uac19\uc740 \ub370\uc774\ud130\ub97c \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n

\"\"<\/p>\n

R\uc5d0\uc11c 2\ucc28 \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub824\uba74 \ub2e4\uc74c \ub2e8\uacc4\ub97c \uc0ac\uc6a9\ud558\uc138\uc694.<\/span><\/p>\n

1\ub2e8\uacc4: \ub370\uc774\ud130\ub97c \uc785\ub825\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n

\uba3c\uc800 \ub370\uc774\ud130\uac00 \ud3ec\ud568\ub41c \ub370\uc774\ud130 \ud504\ub808\uc784\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n

 #createdata<\/span>\ndata <- data.frame(hours=c(6, 9, 12, 14, 30, 35, 40, 47, 51, 55, 60),\n                   happiness=c(14, 28, 50, 70, 89, 94, 90, 75, 59, 44, 27))\n\n#viewdata<\/span> \ndata\n\n   hours happiness\n1 6 14\n2 9 28\n3 12 50\n4 14 70\n5 30 89\n6 35 94\n7 40 90\n8 47 75\n9 51 59\n10 55 44\n11 60 27\n<\/strong><\/pre>\n
 2\ub2e8\uacc4: \ub370\uc774\ud130\ub97c \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n
 \ub2e4\uc74c\uc73c\ub85c, \ub370\uc774\ud130\ub97c \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud574 \uac04\ub2e8\ud55c \uc0b0\uc810\ub3c4\ub97c \ub9cc\ub4e4\uc5b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 #create scatterplot<\/span>\nplot(data$hours, data$happiness, pch=16)<\/strong> <\/pre>\n
\"R\uc758<\/p>\n
 \ub370\uc774\ud130\uac00 \uc120\ud615 \ud328\ud134\uc744 \ub530\ub974\uc9c0 \uc54a\ub294\ub2e4\ub294 \uac83\uc744 \ubd84\uba85\ud788 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 3\ub2e8\uacc4: \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n
 \ub2e4\uc74c\uc73c\ub85c \uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\uc5ec \ub370\uc774\ud130\uc5d0 \uc5bc\ub9c8\ub098 \uc798 \ub9de\ub294\uc9c0 \ud655\uc778\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #fit linear model<\/span>\nlinearModel <- lm(happiness ~ hours, data=data)\n\n#view model summary<\/span>\nsummary(linearModel)\n\nCall:\nlm(formula = happiness ~ hours)\n\nResiduals:\n   Min 1Q Median 3Q Max \n-39.34 -21.99 -2.03 23.50 35.11 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)  \n(Intercept) 48.4531 17.3288 2.796 0.0208 *\nhours 0.2981 0.4599 0.648 0.5331  \n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 28.72 on 9 degrees of freedom\nMultiple R-squared: 0.0446, Adjusted R-squared: -0.06156 \nF-statistic: 0.4201 on 1 and 9 DF, p-value: 0.5331\n<\/strong><\/pre>\n
 \ub2e4\uc911 R-\uc81c\uacf1 \uac12\uc73c\ub85c \ud45c\uc2dc\ub418\ub294 \uac83\ucc98\ub7fc \ubaa8\ub378\uc774 \uc124\uba85\ud558\ub294 \ud589\ubcf5\uc758 \ucd1d \ubd84\uc0b0\uc740 4.46%<\/strong> \uc5d0 \ubd88\uacfc\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 4\ub2e8\uacc4: 2\ucc28 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n
 \ub2e4\uc74c\uc73c\ub85c 2\ucc28 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #create a new variable for hours 2<\/sup><\/span>\ndata$hours2 <- data$hours^2\n\n#fit quadratic regression model<\/span>\nquadraticModel <- lm(happiness ~ hours + hours2, data=data)\n\n#view model summary\n<\/span>summary(quadraticModel)\n\nCall:\nlm(formula = happiness ~ hours + hours2, data = data)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-6.2484 -3.7429 -0.1812 1.1464 13.6678 \n\nCoefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -18.25364 6.18507 -2.951 0.0184 *  \nhours 6.74436 0.48551 13.891 6.98e-07 ***\nhours2 -0.10120 0.00746 -13.565 8.38e-07 ***\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 6.218 on 8 degrees of freedom\nMultiple R-squared: 0.9602, Adjusted R-squared: 0.9502 \nF-statistic: 96.49 on 2 and 8 DF, p-value: 2.51e-06\n<\/strong><\/pre>\n
 \ubaa8\ub378\uc774 \uc124\uba85\ud558\ub294 \ud589\ubcf5\uc758 \ucd1d \ubd84\uc0b0\uc740 96.02%<\/b> \ub85c \uc99d\uac00\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc774 \ub370\uc774\ud130\uc5d0 \uc5bc\ub9c8\ub098 \uc798 \ub9de\ub294\uc9c0 \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span> <\/p>\n
 #create sequence of hour values<\/span>\nhourValues <- seq(0, 60, 0.1)\n\n#create list of predicted happiness levels using quadratic model\n<\/span>happinessPredict <- predict(quadraticModel, list(hours=hourValues, hours2=hourValues^2))\n\n#create scatterplot of original data values\n<\/span>plot(data$hours, data$happiness, pch=16)\n#add predicted lines based on quadratic regression model<\/span>\nlines(hourValues, happinessPredict, col='blue')\n<\/strong><\/pre>\n
\"R\uc758<\/p>\n
 2\ucc28 \ud68c\uadc0\uc120\uc774 \ub370\uc774\ud130 \uac12\uacfc \uaf64 \uc798 \ub9de\ub294 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 5\ub2e8\uacc4: 2\ucc28 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud574\uc11d\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n
 \uc774\uc804 \ub2e8\uacc4\uc5d0\uc11c 2\ucc28 \ud68c\uadc0 \ubaa8\ub378\uc758 \uacb0\uacfc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 Coefficients:\n             Estimate Std. Error t value Pr(>|t|)    \n(Intercept) -18.25364 6.18507 -2.951 0.0184 *  \nhours 6.74436 0.48551 13.891 6.98e-07 ***\nhours2 -0.10120 0.00746 -13.565 8.38e-07 ***\n<\/strong><\/pre>\n
 \uc5ec\uae30\uc5d0 \uc81c\uc2dc\ub41c \uacc4\uc218\ub97c \uae30\ubc18\uc73c\ub85c \uc870\uc815\ub41c 2\ucc28 \ud68c\uadc0\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ud589\ubcf5 = -0.1012(\uc2dc\uac04) 2<\/sup> + 6.7444(\uc2dc\uac04) \u2013 18.2536<\/strong><\/span><\/p>\n
 \uc6b0\ub9ac\ub294 \uc774 \ubc29\uc815\uc2dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \uac1c\uc778\uc758 \uc8fc\ub2f9 \uadfc\ubb34 \uc2dc\uac04\uc744 \ubc14\ud0d5\uc73c\ub85c \uac1c\uc778\uc758 \uc608\uc0c1 \ud589\ubcf5\uc744 \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4, \uc8fc\ub2f9 60\uc2dc\uac04 \uc77c\ud558\ub294 \uc0ac\ub78c\uc758 \ud589\ubcf5 \uc218\uc900\uc740 22.09<\/strong> \uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \ud589\ubcf5 = -0.1012(60) 2<\/sup> + 6.7444(60) \u2013 18.2536 = 22.09<\/span><\/p>\n
 \ubc18\ub300\ub85c \uc8fc\ub2f9 30\uc2dc\uac04 \uc77c\ud558\ub294 \uc0ac\ub78c\uc758 \ud589\ubcf5\uc9c0\uc218\ub294 92.99<\/strong> \uc774\ub2e4.<\/span><\/p>\n
 \ud589\ubcf5 = -0.1012(30) 2<\/sup> + 6.7444(30) \u2013 18.2536 = 92.99<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub450 \ubcc0\uc218\uac00 \uc120\ud615 \uad00\uacc4\ub97c \uac16\ub294 \uacbd\uc6b0 \ub2e8\uc21c \uc120\ud615 \ud68c\uadc0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad00\uacc4\ub97c \uc815\ub7c9\ud654\ud560 \uc218 \uc788\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ub450 \ubcc0\uc218\uac00 2\ucc28 \uad00\uacc4\ub97c \uac16\ub294 \uacbd\uc6b0 2\ucc28 \ud68c\uadc0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uad00\uacc4\ub97c \uc815\ub7c9\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. \uc608: R\uc758 2\ucc28 \ud68c\uadc0 \uadfc\ubb34 \uc2dc\uac04\uacfc \ubcf4\uace0\ub41c \ud589\ubcf5 \uc0ac\uc774\uc758 \uad00\uacc4\ub97c \uc774\ud574\ud558\uace0 \uc2f6\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 11\uba85\uc758 \uc11c\ub85c […]<\/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-781","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nR\uc5d0\uc11c 2\ucc28 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95 - 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