{"id":526,"date":"2023-07-29T14:54:06","date_gmt":"2023-07-29T14:54:06","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8-%e1%84%80%e1%85%ae%e1%84%80%e1%85%a1%e1%86%ab-r\/"},"modified":"2023-07-29T14:54:06","modified_gmt":"2023-07-29T14:54:06","slug":"%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8-%e1%84%80%e1%85%ae%e1%84%80%e1%85%a1%e1%86%ab-r","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%a8%e1%84%8e%e1%85%b3%e1%86%a8-%e1%84%80%e1%85%ae%e1%84%80%e1%85%a1%e1%86%ab-r\/","title":{"rendered":"R\uc5d0\uc11c \uc608\uce21 \uad6c\uac04\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95"},"content":{"rendered":"

<\/p>\n

\n

\uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc740<\/a> \ub2e4\uc74c \ub450 \uac00\uc9c0\uc5d0 \uc720\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

(1)<\/strong> \ud558\ub098 \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uc640 \ubc18\uc751 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc815\ub7c9\ud654\ud569\ub2c8\ub2e4.<\/span><\/p>\n

(2)<\/b> \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubbf8\ub798 \uac00\uce58\ub97c \uc608\uce21\ud569\ub2c8\ub2e4.<\/span><\/p>\n

(2)<\/strong> \uc640 \uad00\ub828\ud558\uc5ec \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubbf8\ub798 \uac12\uc744 \uc608\uce21\ud560 \ub54c \uc815\ud655\ud55c \uac12<\/em> \uacfc \uac00\ub2a5\ud55c \uac12\uc758 \ubc94\uc704\ub97c \ud3ec\ud568\ud558\ub294 \uad6c\uac04\uc744<\/em> \ubaa8\ub450 \uc608\uce21\ud558\ub824\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \uc774 \uac04\uaca9\uc744 \uc608\uce21 \uac04\uaca9<\/strong> \uc774\ub77c\uace0 \ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc608\ub97c \ub4e4\uc5b4, \ud559\uc2b5 \uc2dc\uac04\uc744<\/em> \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \uc2dc\ud5d8 \uc810\uc218\ub97c<\/em> \uc751\ub2f5 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\ud569\ud558\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uba74 6\uc2dc\uac04 \ub3d9\uc548 \uacf5\ubd80\ud558\ub294 \ud559\uc0dd\uc774 \uc2dc\ud5d8\uc5d0\uc11c 91<\/strong> \uc810\uc744 \ubc1b\uc744 \uac83\uc774\ub77c\uace0 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uadf8\ub7ec\ub098 \uc774 \uc608\uce21\uc5d0\ub294 \ubd88\ud655\uc2e4\uc131\uc774 \uc788\uc73c\ubbc0\ub85c 6\uc2dc\uac04 \ub3d9\uc548 \uacf5\ubd80\ud558\ub294 \ud559\uc0dd\uc774 85<\/strong> \uc5d0\uc11c 97<\/strong> \uc0ac\uc774\uc758 \uc2dc\ud5d8 \uc810\uc218\ub97c \uc5bb\uc744 \ud655\ub960\uc774 95%\uc784\uc744 \ub098\ud0c0\ub0b4\ub294 \uc608\uce21 \uad6c\uac04\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac12 \ubc94\uc704\ub294 95% \uc608\uce21 \uad6c\uac04\uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc73c\uba70 \uc815\ud655\ud55c \uc608\uce21 \uac12\uc744 \uc544\ub294 \uac83\ubcf4\ub2e4 \uc6b0\ub9ac\uc5d0\uac8c \ub354 \uc720\uc6a9\ud55c \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

R\uc5d0\uc11c \uc608\uce21 \uad6c\uac04\uc744 \ub9cc\ub4dc\ub294 \ubc29\ubc95<\/strong><\/span><\/h2>\n

R\uc5d0\uc11c \uc608\uce21 \uad6c\uac04\uc744 \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud558\uae30 \uc704\ud574 \uc5ec\ub7ec \uac00\uc9c0 \uc790\ub3d9\ucc28\uc758 \ud2b9\uc131\uc5d0 \ub300\ud55c \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c \ub0b4\uc7a5 mtcars<\/em> \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n

 #view first six rows of<\/span> mtcars<\/em><\/span>\nhead(mtcars)\n\n# mpg cyl disp hp drat wt qsec vs am gear carb\n#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4\n#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4\n#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1\n#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1\n#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2\n#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1\n<\/strong><\/pre>\n
 \uba3c\uc800, \uc608\uce21 \ubcc0\uc218\ub85c disp\ub97c<\/em> \uc0ac\uc6a9\ud558\uace0 \uc751\ub2f5 \ubcc0\uc218\ub85c mpg\ub97c<\/em> \uc0ac\uc6a9\ud558\uc5ec \uac04\ub2e8\ud55c \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud53c\ud305\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #fit simple linear regression model<\/span>\nmodel <- lm(mpg ~ disp, data = mtcars)\n\n#view summary of fitted model<\/span>\nsummary(model)\n\n#Call:\n#lm(formula = mpg ~ availability, data = mtcars)\n#\n#Residuals:\n# Min 1Q Median 3Q Max \n#-4.8922 -2.2022 -0.9631 1.6272 7.2305 \n#\n#Coefficients:\n#Estimate Std. Error t value Pr(>|t|)    \n#(Intercept) 29.599855 1.229720 24.070 < 2e-16 ***\n#disp -0.041215 0.004712 -8.747 9.38e-10 ***\n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n#\n#Residual standard error: 3.251 on 30 degrees of freedom\n#Multiple R-squared: 0.7183, Adjusted R-squared: 0.709 \n#F-statistic: 76.51 on 1 and 30 DF, p-value: 9.38e-10\n<\/strong><\/pre>\n
 \ub2e4\uc74c\uc73c\ub85c, \uc801\ud569 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec disp<\/em> \uc5d0 \ub300\ud55c \uc138 \uac00\uc9c0 \uc0c8\ub85c\uc6b4 \uac12\uc744 \uae30\ubc18\uc73c\ub85c mpg<\/em> \uac12\uc744 \uc608\uce21\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #create data frame with three new values for<\/span> avail\n<\/span><\/em>new_disp <- data.frame(disp= c(150, 200, 250))\n\n#use the fitted model to predict the value for mpg<\/em><\/span> based on the three new values<\/span>\n#for<\/span> avail<\/span>\n<\/em>predict(model, newdata = new_disp)\n\n#1 2 3 \n#23.41759 21.35683 19.29607 \n<\/strong><\/pre>\n
 \uc774 \uac12\uc744 \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
    \n
  •  EPA\uac00<\/em> 150\uc778 \uc0c8 \uc790\ub3d9\ucc28\uc758 \uacbd\uc6b0 mpg<\/em> \ub294 23.41759<\/strong> \uac00 \ub420 \uac83\uc73c\ub85c \uc608\uc0c1\ub429\ub2c8\ub2e4.<\/span><\/li>\n
  •  EPA\uac00<\/em> 200\uc778 \uc0c8 \uc790\ub3d9\ucc28\uc758 \uacbd\uc6b0 mpg<\/em> \ub294 21.35683<\/strong> \uc774 \ub420 \uac83\uc73c\ub85c \uc608\uc0c1\ub429\ub2c8\ub2e4.<\/span><\/li>\n
  •  EPA\uac00<\/em> 250\uc778 \uc0c8 \uc790\ub3d9\ucc28\uc758 \uacbd\uc6b0 mpg<\/em> \ub294 19.29607<\/strong> \uc774 \ub420 \uac83\uc73c\ub85c \uc608\uc0c1\ub429\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n
     \ub2e4\uc74c\uc73c\ub85c, \uc801\ud569 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\ub7ec\ud55c \uc608\uce21 \uac12 \uc8fc\uc704\uc758 \uc608\uce21 \uad6c\uac04\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n
     #create prediction intervals around the predicted values<\/span>\npredict(model, newdata = new_disp, interval = \" predict<\/span> \")<\/span>\n\n# fit lwr upr\n#1 23.41759 16.62968 30.20549\n#2 21.35683 14.60704 28.10662\n#3 19.29607 12.55021 26.04194\n<\/strong><\/pre>\n
     \uc774 \uac12\uc744 \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
      \n
    •  EPA<\/em> \uac00 150\uc778 \uc790\ub3d9\ucc28\uc758 95% mpg<\/em> \uc608\uce21 \uad6c\uac04\uc740 16.62968<\/strong> \uc5d0\uc11c 30.20549<\/strong> \uc0ac\uc774\uc785\ub2c8\ub2e4.<\/span><\/li>\n
    •  EPA<\/em> \uac00 200\uc778 \uc790\ub3d9\ucc28\uc758 95% mpg<\/em> \uc608\uce21 \uad6c\uac04\uc740 14.60704<\/b> \uc5d0\uc11c 28.10662<\/strong> \uc0ac\uc774\uc785\ub2c8\ub2e4.<\/span><\/li>\n
    •  EPA<\/em> \uac00 250\uc778 \uc790\ub3d9\ucc28\uc758 95% mpg<\/em> \uc608\uce21 \uad6c\uac04\uc740 12.55021<\/b> \uc5d0\uc11c 26.04194<\/strong> \uc0ac\uc774\uc785\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n
       \uae30\ubcf8\uc801\uc73c\ub85c R\uc740 95% \uc608\uce21 \uad6c\uac04\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 level<\/strong> \uba85\ub839\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\ub97c \uc6d0\ud558\ub294 \ub300\ub85c \ubcc0\uacbd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \ub2e4\uc74c \ucf54\ub4dc\ub294 99% \uc608\uce21 \uad6c\uac04\uc744 \uc0dd\uc131\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
       #create 99% prediction intervals around the predicted values\n<\/span>predict(model, newdata = new_disp, interval = \" predict<\/span> \", level = 0.99<\/span><\/span><\/span> )<\/span>\n\n# fit lwr upr\n#1 23.41759 14.27742 32.55775\n#2 21.35683 12.26799 30.44567\n#3 19.29607 10.21252 28.37963\n<\/strong><\/pre>\n
       99% \uc608\uce21 \uad6c\uac04\uc740 95% \uc608\uce21 \uad6c\uac04\ubcf4\ub2e4 \ub113\uc2b5\ub2c8\ub2e4. \uac04\uaca9\uc774 \ub113\uc744\uc218\ub85d \uc608\uce21 \uac12\uc774 \ud3ec\ud568\ub420 \uac00\ub2a5\uc131\uc774 \ub192\uae30 \ub54c\ubb38\uc5d0 \uc774\ub294 \uc758\ubbf8\uac00 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
       R\uc5d0\uc11c \uc608\uce21 \uad6c\uac04\uc744 \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95<\/strong><\/span><\/h2>\n
       \ub2e4\uc74c \ucf54\ub4dc\ub294 \ub2e4\uc74c \uae30\ub2a5\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucc28\ud2b8\ub97c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
        \n
      •  \uac00\uc6a9\uc131<\/em> \ubc0f mpg<\/em> \uc5d0 \ub300\ud55c \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 \uc0b0\uc810\ub3c4<\/span><\/li>\n
      •  \uc801\ud569 \ud68c\uadc0\uc120\uc5d0 \ub300\ud55c \ud30c\ub780\uc0c9 \uc120<\/span><\/li>\n
      •  \ud68c\uc0c9 \uc2e0\ub8b0 \ubc34\ub4dc<\/span><\/li>\n
      •  \ube68\uac04\uc0c9 \uc608\uce21 \ubc34\ub4dc<\/span><\/li>\n<\/ul>\n
         #define dataset<\/span>\ndata <- mtcars[, c(\"mpg\", \"disp\")]\n\n#create simple linear regression model\n<\/span>model <- lm(mpg ~ disp, data = mtcars)\n\n#use model to create prediction intervals\n<\/span>predictions <- predict(model, interval = \" predict<\/span> \")\n\n#create dataset that contains original data along with prediction intervals\n<\/span>all_data <- cbind(data, predictions)\n\n#load ggplot2<\/em> library\n<\/span>library(ggplot2)\n\n#createplot\n<\/span>ggplot(all_data, aes(x = disp, y = mpg)) + #define x and y axis variables<\/span>\n  geom_point() + #add scatterplot points<\/span>\n  stat_smooth(method = lm) + #confidence bands<\/span>\n  geom_line(aes(y = lwr), col = \"coral2\", linetype = \"dashed\") + #lwr pred interval<\/span>\n  geom_line(aes(y = upr), col = \"coral2\", linetype = \"dashed\") #upr pred interval<\/span><\/strong><\/pre>\n
         \uc2e0\ub8b0 \uad6c\uac04\uacfc \uc608\uce21 \uad6c\uac04\uc744 \uc0ac\uc6a9\ud558\ub294 \uacbd\uc6b0<\/span><\/strong><\/h2>\n
         \uc608\uce21 \uad6c\uac04\uc740<\/strong> \ub2e8\uc77c \uac12\uc5d0 \ub300\ud55c \ubd88\ud655\uc2e4\uc131\uc744 \ud3ec\ucc29\ud569\ub2c8\ub2e4. \uc2e0\ub8b0 \uad6c\uac04\uc740<\/strong> \uc608\uce21\ub41c \ud3c9\uade0\uac12\uc5d0 \ub300\ud55c \ubd88\ud655\uc2e4\uc131\uc744 \ud3ec\ucc29\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc608\uce21 \uad6c\uac04\uc740 \ub3d9\uc77c\ud55c \uac12\uc5d0 \ub300\ud55c \uc2e0\ub8b0 \uad6c\uac04\ubcf4\ub2e4 \ud56d\uc0c1 \ub113\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
         \ud2b9\uc815 \uac1c\ubcc4 \uc608\uce21\uc5d0 \uad00\uc2ec\uc774 \uc788\ub294 \uacbd\uc6b0 \uc608\uce21 \uad6c\uac04\uc744 \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4. \uc2e0\ub8b0 \uad6c\uac04\uc740 \ub108\ubb34 \uc881\uc740 \uac12 \ubc94\uc704\ub97c \uc0dd\uc131\ud558\uc5ec \uad6c\uac04\uc5d0 \uc2e4\uc81c \uac12\uc774 \ud3ec\ud568\ub418\uc9c0 \uc54a\uc744 \ud655\ub960\uc774 \ub354 \ucee4\uc9c0\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
        \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc740 \ub2e4\uc74c \ub450 \uac00\uc9c0\uc5d0 \uc720\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. (1) \ud558\ub098 \uc774\uc0c1\uc758 \uc608\uce21 \ubcc0\uc218\uc640 \ubc18\uc751 \ubcc0\uc218 \uac04\uc758 \uad00\uacc4\ub97c \uc815\ub7c9\ud654\ud569\ub2c8\ub2e4. (2) \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubbf8\ub798 \uac00\uce58\ub97c \uc608\uce21\ud569\ub2c8\ub2e4. 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