{"id":485,"date":"2023-07-29T18:02:44","date_gmt":"2023-07-29T18:02:44","guid":{"rendered":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%a1%e1%84%82%e1%85%a9%e1%84%87%e1%85%a1-%e1%84%85%e1%85%a1-%e1%84%8b%e1%85%a3%e1%86%bc%e1%84%87%e1%85%a1%e1%86%bc%e1%84%92%e1%85%a3%e1%86%bc\/"},"modified":"2023-07-29T18:02:44","modified_gmt":"2023-07-29T18:02:44","slug":"%e1%84%8b%e1%85%a1%e1%84%82%e1%85%a9%e1%84%87%e1%85%a1-%e1%84%85%e1%85%a1-%e1%84%8b%e1%85%a3%e1%86%bc%e1%84%87%e1%85%a1%e1%86%bc%e1%84%92%e1%85%a3%e1%86%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/%e1%84%8b%e1%85%a1%e1%84%82%e1%85%a9%e1%84%87%e1%85%a1-%e1%84%85%e1%85%a1-%e1%84%8b%e1%85%a3%e1%86%bc%e1%84%87%e1%85%a1%e1%86%bc%e1%84%92%e1%85%a3%e1%86%bc\/","title":{"rendered":"R\uc5d0\uc11c \uc591\ubc29\ud5a5 anova\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"

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\uc591\ubc29\ud5a5 ANOVA<\/a> (“\ubd84\uc0b0 \ubd84\uc11d”)\ub294 \ub450 \uc694\uc778\uc5d0 \uac78\uccd0 \ubd84\ud560\ub41c 3\uac1c \uc774\uc0c1\uc758 \ub3c5\ub9bd \uadf8\ub8f9\uc758 \ud3c9\uade0 \uac04\uc5d0 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \ucc28\uc774\uac00 \uc788\ub294\uc9c0 \uc5ec\ubd80\ub97c \ud655\uc778\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \uc591\ubc29\ud5a5 ANOVA\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc608: R\uc758 \uc591\ubc29\ud5a5 ANOVA<\/strong><\/span><\/h3>\n

\uc6b4\ub3d9 \uac15\ub3c4\uc640 \uc131\ubcc4\uc774 \uccb4\uc911 \uac10\ub7c9\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294\uc9c0 \ud655\uc778\ud558\uace0 \uc2f6\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacbd\uc6b0 \uc6b0\ub9ac\uac00 \ubcf4\uace0 \uc788\ub294 \ub450 \uac00\uc9c0 \uc694\uc18c\ub294 \uc6b4\ub3d9<\/em> \uacfc \uc131\ubcc4<\/em> \uc774\uba70, \uc751\ub2f5 \ubcc0\uc218\ub294 \ud30c\uc6b4\ub4dc \ub2e8\uc704\ub85c \uce21\uc815\ub418\ub294 \uccb4\uc911 \uac10\uc18c<\/em> \uc785\ub2c8\ub2e4.<\/span><\/p>\n

\uc6b4\ub3d9\uacfc \uc131\ubcc4\uc774 \uccb4\uc911 \uac10\ub7c9\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294\uc9c0 \ud655\uc778\ud558\uace0 \uc6b4\ub3d9\uacfc \uc131\ubcc4\uc774 \uccb4\uc911 \uac10\ub7c9\uc5d0 \uc0c1\ud638 \uc791\uc6a9\uc774 \uc788\ub294\uc9c0 \ud655\uc778\ud558\uae30 \uc704\ud574 \uc591\ubc29\ud5a5 \ubd84\uc0b0 \ubd84\uc11d\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\uc6b0\ub9ac\ub294 \ub0a8\uc131 30\uba85, \uc5ec\uc131 30\uba85\uc744 \ubb34\uc791\uc704\ub85c 10\uba85\uc529 \ubb34\uc791\uc704\ub85c \ud560\ub2f9\ud558\uc5ec \ud55c \ub2ec \ub3d9\uc548 \uc6b4\ub3d9\uc744 \ud558\uc9c0 \uc54a\uac70\ub098 \uac00\ubcbc\uc6b4 \uc6b4\ub3d9 \ub610\ub294 \uaca9\ub82c\ud55c \uc6b4\ub3d9 \ud504\ub85c\uadf8\ub7a8\uc744 \ub530\ub974\ub294 \uc2e4\ud5d8\uc5d0 \ucc38\uc5ec\ud558\ub3c4\ub85d \ubaa8\uc9d1\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc74c \ucf54\ub4dc\ub294 \uc6b0\ub9ac\uac00 \uc791\uc5c5\ud560 \ub370\uc774\ud130 \ud504\ub808\uc784\uc744 \uc0dd\uc131\ud569\ub2c8\ub2e4.<\/span><\/p>\n

 #make this example reproducible\n<\/span>set.seed(10)\n\n#create data frame\n<\/span>data <- data.frame(gender = rep(c(\"Male\", \"Female\"), each = 30),\n                   exercise = rep(c(\"None\", \"Light\", \"Intense\"), each = 10, times = 2),\n                   weight_loss = c(runif(10, -3, 3), runif(10, 0, 5), runif(10, 5, 9),\n                                   runif(10, -4, 2), runif(10, 0, 3), runif(10, 3, 8)))\n\n#view first six rows of data frame\n<\/span>head(data)\n\n# gender exercise weight_loss\n#1 Male None 0.04486922\n#2 Male None -1.15938896\n#3 Male None -0.43855400\n#4 Male None 1.15861249\n#5 Male None -2.48918419\n#6 Male None -1.64738030\n\n#see how many participants are in each group<\/span>\ntable(data$gender, data$exercise)\n\n# Intense Light None\n# Female 10 10 10\n# Male 10 10 10\n<\/strong><\/pre>\n
 \ub370\uc774\ud130 \ud0d0\uc0c9<\/strong><\/span><\/h3>\n
 \uc591\ubc29\ud5a5 ANOVA \ubaa8\ub378\uc744 \uc801\uc6a9\ud558\uae30 \uc804\uc5d0 dplyr<\/strong> \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec 6\uac1c \uce58\ub8cc \uadf8\ub8f9 \uac01\uac01\uc5d0 \ub300\ud55c \uccb4\uc911 \uac10\uc18c\uc758 \ud3c9\uade0 \ubc0f \ud45c\uc900 \ud3b8\ucc28\ub97c \ucc3e\uc544 \ub370\uc774\ud130\ub97c \ub354 \uc798 \uc774\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #load dplyr<\/em> package<\/span>\nlibrary(dplyr)\n\n#find mean and standard deviation of weight loss for each treatment group<\/span>\ndata %>%\n  group_by<\/span> (gender, exercise) %>%\n  summarize<\/span> (mean = mean(weight_loss),\n            sd = sd(weight_loss))\n\n# A tibble: 6 x 4\n# Groups: gender [2]\n# gender exercise means sd\n#          \n#1 Female Intense 5.31 1.02 \n#2 Female Light 0.920 0.835\n#3 Female None -0.501 1.77 \n#4 Male Intense 7.37 0.928\n#5 Male Light 2.13 1.22 \n#6 Male None -0.698 1.12 \n<\/strong><\/pre>\n
 #set margins so that axis labels on boxplot don't get cut off<\/span>\nby(mar=c(8, 4.1, 4.1, 2.1))\n\n#create boxplots\n<\/span>boxplot(weight_loss ~ gender:exercise,\ndata = data,\nmain = \"Weight Loss Distribution by Group\",\nxlab = \"Group\",\nylab = \"Weight Loss\",\ncol = \"steelblue\",\nborder = \"black\", \nlas = 2 #make x-axis labels perpendicular<\/span>\n)<\/strong><\/pre>\n
 \uac15\ub82c\ud55c<\/em> \uc6b4\ub3d9\uc5d0 \ucc38\uc5ec\ud55c \ub450 \uadf8\ub8f9\uc758 \uccb4\uc911 \uac10\ub7c9 \uac12\uc774 \ub354 \ub192\uc740 \uac83\uc73c\ub85c \uc989\uc2dc \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c \uac15\ub82c\ud55c<\/em> \uc6b4\ub3d9 \uadf8\ub8f9\uacfc \uac00\ubcbc\uc6b4<\/em> \uc6b4\ub3d9 \uadf8\ub8f9 \ubaa8\ub450\uc5d0\uc11c \ub0a8\uc131\uc774 \uc5ec\uc131\ubcf4\ub2e4 \uccb4\uc911 \uac10\ub7c9 \uac12\uc774 \ub354 \ub192\uc740 \uacbd\ud5a5\uc774 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ub2e4\uc74c\uc73c\ub85c, \uc774\ub7ec\ud55c \uc2dc\uac01\uc801 \ucc28\uc774\uac00 \uc2e4\uc81c\ub85c \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ud55c\uc9c0 \ud655\uc778\ud558\uae30 \uc704\ud574 \uc591\ubc29\ud5a5 ANOVA \ubaa8\ub378\uc744 \ub370\uc774\ud130\uc5d0 \uc801\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc591\ubc29\ud5a5 ANOVA \ubaa8\ub378 \ud53c\ud305<\/strong><\/span><\/h3>\n
 R\uc5d0\uc11c \uc591\ubc29\ud5a5 ANOVA \ubaa8\ub378\uc744 \ud53c\ud305\ud558\ub294 \uc77c\ubc18\uc801\uc778 \uad6c\ubb38\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 aov(\uc751\ub2f5 \ubcc0\uc218 ~predictor_variable1 *predictor_variable2, \ub370\uc774\ud130 = \ub370\uc774\ud130 \uc138\ud2b8)<\/span><\/strong><\/p>\n
 \ub450 \uc608\uce21 \ubcc0\uc218 \uc0ac\uc774\uc758 *<\/strong> \ub294 \ub450 \uc608\uce21 \ubcc0\uc218 \uac04\uc758 \uc0c1\ud638 \uc791\uc6a9 \ud6a8\uacfc\ub3c4 \ud14c\uc2a4\ud2b8\ud558\uace0 \uc2f6\ub2e4\ub294 \uac83\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uc608\uc5d0\uc11c\ub294 \uccb4\uc911 \uac10\ub7c9\uc744<\/em> \uc751\ub2f5 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uace0 \uc131\ubcc4<\/em> \uacfc \uc6b4\ub3d9\uc744<\/em> \ub450 \uc608\uce21 \ubcc0\uc218\ub85c \uc0ac\uc6a9\ud558\uc5ec \uc591\ubc29\ud5a5 ANOVA \ubaa8\ub378\uc744 \ub9de\ucd94\uae30 \uc704\ud574 \ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uadf8\ub7f0 \ub2e4\uc74c summary()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378 \uacb0\uacfc\ub97c \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #fit the two-way ANOVA model<\/span>\nmodel <- aov(weight_loss ~ gender * exercise, data = data)\n\n#view the model output<\/span>\nsummary(model)\n\n# Df Sum Sq Mean Sq F value Pr(>F)    \n#gender 1 15.8 15.80 11.197 0.0015 ** \n#exercise 2 505.6 252.78 179.087 <2e-16 ***\n#gender:exercise 2 13.0 6.51 4.615 0.0141 *  \n#Residuals 54 76.2 1.41                   \n#---\n#Significant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n<\/strong><\/pre>\n
 \ubaa8\ub378 \uacb0\uacfc\ub97c \ubcf4\uba74 \uc131\ubcc4<\/em> , \uc6b4\ub3d9<\/em> , \ub450 \ubcc0\uc218 \uac04\uc758 \uc0c1\ud638\uc791\uc6a9\uc774 \ubaa8\ub450 \uc720\uc758\uc218\uc900 0.05\uc5d0\uc11c \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \uac83\uc73c\ub85c \ub098\ud0c0\ub0ac\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ubaa8\ub378 \uac00\uc815 \ud655\uc778<\/strong><\/span><\/h3>\n
 \ub354 \ub098\uc544\uac00\uae30 \uc804\uc5d0 \ubaa8\ub378 \uacb0\uacfc\uac00 \uc2e0\ub8b0\ud560 \uc218 \uc788\ub3c4\ub85d \ubaa8\ub378\uc758 \uac00\uc815\uc774 \ucda9\uc871\ub418\ub294\uc9c0 \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4. \ud2b9\ud788 \uc591\ubc29\ud5a5 ANOVA\ub294 \ub2e4\uc74c\uc744 \uac00\uc815\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 1. \ub3c5\ub9bd\uc131<\/strong> – \uac01 \uadf8\ub8f9\uc758 \uad00\ucc30\uc740 \uc11c\ub85c \ub3c5\ub9bd\uc801\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/span> \ubb34\uc791\uc704 \uc124\uacc4\ub97c \uc0ac\uc6a9\ud588\uae30 \ub54c\ubb38\uc5d0<\/span> \uc774 \uac00\uc815\uc774 \ucda9\uc871\ub418\uc5b4\uc57c \ud558\ubbc0\ub85c \ud06c\uac8c \uac71\uc815\ud560 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 2. \uc815\uaddc\uc131<\/strong> – \uc885\uc18d \ubcc0\uc218\ub294 \ub450 \uc694\uc778 \uadf8\ub8f9\uc758 \uac01 \uc870\ud569\uc5d0 \ub300\ud574 \ub300\ub7b5\uc801\uc778 \uc815\uaddc \ubd84\ud3ec\ub97c \uac00\uc838\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uac00\uc815\uc744 \ud14c\uc2a4\ud2b8\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 \ubaa8\ub378 \uc794\ucc28\uc758 \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \ub9cc\ub4dc\ub294 \uac83\uc785\ub2c8\ub2e4. \uc794\ucc28\uac00 \ub300\ub7b5\uc801\uc73c\ub85c \uc815\uaddc \ubd84\ud3ec\ub97c \ub530\ub974\ub294 \uacbd\uc6b0 \uc774 \uac00\uc815\uc774 \ucda9\uc871\ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #define model residuals\n<\/b><\/span>reside <- model$residuals<\/strong>\n\n#create histogram of residuals<\/strong><\/span>\nhist(resid, main = \"Histogram of Residuals\", xlab = \"Residuals\", col = \"steelblue\")<\/strong><\/pre>\n
 \uc794\ucc28\ub294 \ub300\ub7b5 \uc815\uaddc \ubd84\ud3ec\ub97c \ub530\ub974\ubbc0\ub85c \uc815\uaddc\uc131 \uac00\uc815\uc774 \ucda9\uc871\ub41c\ub2e4\uace0 \uac00\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 3. \ub4f1\ubd84\uc0b0<\/strong> – \uac01 \uadf8\ub8f9\uc758 \ubd84\uc0b0\uc774 \ub3d9\uc77c\ud558\uac70\ub098 \uac70\uc758 \ub3d9\uc77c\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774 \uac00\uc815\uc744 \ud655\uc778\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 car<\/strong> \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub4f1\ubd84\uc0b0 \ud14c\uc2a4\ud2b8\ub97c \uc218\ud589\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 #load car<\/em> package<\/span>\nlibrary(car)\n\n#conduct Levene's Test for equality of variances<\/span>\nleveneTest(weight_loss ~ gender * exercise, data = data)\n\n#Levene's Test for Homogeneity of Variance (center = median)\n# Df F value Pr(>F)\n#group 5 1.8547 0.1177\n#54  \n<\/strong><\/pre>\n
 \uac80\uc815\uc758 p-\uac12\uc774 \uc720\uc758 \uc218\uc900 0.05\ubcf4\ub2e4 \ud06c\ubbc0\ub85c \uadf8\ub8f9 \uac04 \ubd84\uc0b0 \ub3d9\uc77c\uc131 \uac00\uc815\uc774 \ucda9\uc871\ub41c\ub2e4\uace0 \uac00\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uce58\ub8cc \ucc28\uc774\uc810 \ubd84\uc11d<\/strong><\/span><\/h3>\n
 \ubaa8\ub378 \uac00\uc815\uc774 \ucda9\uc871\ub418\ub294\uc9c0 \ud655\uc778\ud55c \ud6c4\uc5d0\ub294 \uc0ac\ud6c4 \ud14c\uc2a4\ud2b8\ub97c<\/a> \uc218\ud589\ud558\uc5ec \uc815\ud655\ud788 \uc5b4\ub5a4 \uce58\ub8cc \uadf8\ub8f9\uc774 \uc11c\ub85c \ub2e4\ub978\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc0ac\ud6c4 \ud14c\uc2a4\ud2b8\uc5d0\uc11c\ub294 TukeyHSD()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc911 \ube44\uad50\ub97c \uc704\ud55c Tukey \ud14c\uc2a4\ud2b8\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/span><\/p>\n
 #perform Tukey's Test for multiple comparisons\n<\/span>TukeyHSD(model, conf.level=.95) \n\n#Tukey multiple comparisons of means\n# 95% family-wise confidence level\n#\n#Fit: aov(formula = weight_loss ~ gender * exercise, data = data)\n#\n#$gender\n# diff lwr upr p adj\n#Male-Female 1.026456 0.4114451 1.641467 0.0014967\n#\n#$exercise\n# diff lwr upr p adj\n#Light-Intense -4.813064 -5.718493 -3.907635 0.0e+00\n#None-Intense -6.938966 -7.844395 -6.033537 0.0e+00\n#None-Light -2.125902 -3.031331 -1.220473 1.8e-06\n#\n#$`gender:exercise`\n# diff lwr upr p adj\n#Male:Intense-Female:Intense 2.0628297 0.4930588 3.63260067 0.0036746\n#Female:Light-Female:Intense -4.3883563 -5.9581272 -2.81858535 0.0000000\n#Male:Light-Female:Intense -3.1749419 -4.7447128 -1.60517092 0.0000027\n#Female:None-Female:Intense -5.8091131 -7.3788841 -4.23934219 0.0000000\n#Male:None-Female:Intense -6.0059891 -7.5757600 -4.43621813 0.0000000\n#Female:Light-Male:Intense -6.4511860 -8.0209570 -4.88141508 0.0000000\n#Male:Light-Male:Intense -5.2377716 -6.8075425 -3.66800066 0.0000000\n#Female:None-Male:Intense -7.8719429 -9.4417138 -6.30217192 0.0000000\n#Male:None-Male:Intense -8.0688188 -9.6385897 -6.49904786 0.0000000\n#Male:Light-Female:Light 1.2134144 -0.3563565 2.78318536 0.2185439\n#Female:None-Female:Light -1.4207568 -2.9905278 0.14901410 0.0974193\n#Male:None-Female:Light -1.6176328 -3.1874037 -0.04786184 0.0398106\n#Female:None-Male:Light -2.6341713 -4.2039422 -1.06440032 0.0001050\n#Male:None-Male:Light -2.8310472 -4.4008181 -1.26127627 0.0000284\n#Male:None-Female:None -0.1968759 -1.7666469 1.37289500 0.9990364<\/strong><\/pre>\n
 p-\uac12\uc740 \uac01 \uadf8\ub8f9 \uac04\uc5d0 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ud55c \ucc28\uc774\uac00 \uc788\ub294\uc9c0 \uc5ec\ubd80\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc608\ub97c \ub4e4\uc5b4, \uc704\uc758 \ub9c8\uc9c0\ub9c9 \ud589\uc5d0\uc11c \uc6b4\ub3d9\uc744 \ud558\uc9c0 \uc54a\uc740 \ub0a8\uc131 \uadf8\ub8f9\uc740 \uc6b4\ub3d9\uc744 \ud558\uc9c0 \uc54a\uc740 \uc5ec\uc131 \uadf8\ub8f9\uc5d0 \ube44\ud574 \uccb4\uc911 \uac10\ub7c9\uc5d0 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \ucc28\uc774\uac00 \uc5c6\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4(p-\uac12: 0.990364).<\/span><\/p>\n
 \ub610\ud55c R\uc758 plot()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec Tukey \ud14c\uc2a4\ud2b8\uc758 \uacb0\uacfc\uc778 95% \uc2e0\ub8b0 \uad6c\uac04\uc744 \uc2dc\uac01\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #set axis margins so labels don't get cut off\n<\/span>by(mar=c(4.1, 13, 4.1, 2.1))\n\n#create confidence interval for each comparison\n<\/span>plot(TukeyHSD(model, conf.level=.95), las = 2)\n<\/strong><\/pre>\n
 \uc591\ubc29\ud5a5 ANOVA \uacb0\uacfc \ubcf4\uace0<\/span><\/strong><\/h3>\n
 \ub9c8\uc9c0\ub9c9\uc73c\ub85c \uacb0\uacfc\ub97c \uc694\uc57d\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc591\ubc29\ud5a5 ANOVA\uc758 \uacb0\uacfc\ub97c \ubcf4\uace0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc131\ubcc4( \ub0a8\uc131, \uc5ec\uc131)<\/em> \ubc0f \uc6b4\ub3d9 \ud504\ub85c\uadf8\ub7a8 (\uc5c6\uc74c, \uac00\ubcbc\uc6b4 \uc6b4\ub3d9, \uac15\ub82c\ud55c \uc6b4\ub3d9)\uc774<\/em> \uccb4\uc911 \uac10\ub7c9 (\ud30c\uc6b4\ub4dc\ub85c \uce21\uc815)\uc5d0 \ubbf8\uce58\ub294 \uc601\ud5a5\uc744 \uc870\uc0ac\ud558\uae30 \uc704\ud574 \uc591\ubc29\ud5a5 ANOVA\ub97c \uc218\ud589\ud588\uc2b5\ub2c8\ub2e4.<\/em> \uc131\ubcc4\uacfc \uc6b4\ub3d9\uc774 \uccb4\uc911 \uac10\ub7c9\uc5d0 \ubbf8\uce58\ub294 \uc601\ud5a5 \uc0ac\uc774\uc5d0\ub294 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \uc0c1\ud638\uc791\uc6a9\uc774 \uc788\uc5c8\uc2b5\ub2c8\ub2e4(F(2, 54) = 4.615, p = 0.0141).<\/span> \uc0ac\ud6c4 Tukey\uc758 HSD \ud14c\uc2a4\ud2b8\uac00 \uc218\ud589\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ub0a8\uc131\uc758 \uacbd\uc6b0, \uac15\ub82c\ud55c<\/em> \uc6b4\ub3d9 \ud504\ub85c\uadf8\ub7a8\uc740 \uac00\ubcbc\uc6b4<\/em> \ud504\ub85c\uadf8\ub7a8(p < 0.0001)\uc774\ub098 \uc6b4\ub3d9 \ud504\ub85c\uadf8\ub7a8\uc744 \ud558\uc9c0 \uc54a\ub294 \uac83<\/em> (p < 0.0001)\uc5d0 \ube44\ud574 \uccb4\uc911 \uac10\ub7c9 \ud6a8\uacfc\uac00 \ud6e8\uc52c \ucef8\uc2b5\ub2c8\ub2e4. \ub610\ud55c \ub0a8\uc131\uc758 \uacbd\uc6b0 \uac00\ubcbc\uc6b4<\/em> \uc2dd\uc0ac\ub294 \uc6b4\ub3d9\uc744 \ud558\uc9c0 \uc54a\ub294 \uac83<\/em> \ubcf4\ub2e4 \ud6e8\uc52c \ub354 \ud070 \uccb4\uc911 \uac10\uc18c\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4(p < 0.0001).<\/span><\/p>\n
 \uc5ec\uc131\uc758 \uacbd\uc6b0, \uac15\ub82c\ud55c<\/em> \uc6b4\ub3d9 \ud504\ub85c\uadf8\ub7a8\uc740 \uac00\ubcbc\uc6b4<\/em> \ud504\ub85c\uadf8\ub7a8(p < 0.0001)\uc774\ub098 \uc6b4\ub3d9 \ud504\ub85c\uadf8\ub7a8\uc744 \ud558\uc9c0 \uc54a\ub294 \uac83<\/em> (p < 0.0001)\uc5d0 \ube44\ud574 \ud6e8\uc52c \ub354 \ud070 \uccb4\uc911 \uac10\uc18c\ub97c \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 ANOVA\uc758 \uac00\uc815\uc774 \ucda9\uc871\ub418\ub294\uc9c0 \ud655\uc778\ud558\uae30 \uc704\ud574 \uc815\uaddc\uc131 \uac80\uc0ac\uc640 Levene \ud14c\uc2a4\ud2b8\ub97c \uc218\ud589\ud588\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
\uc591\ubc29\ud5a5 ANOVA (“\ubd84\uc0b0 \ubd84\uc11d”)\ub294 \ub450 \uc694\uc778\uc5d0 \uac78\uccd0 \ubd84\ud560\ub41c 3\uac1c \uc774\uc0c1\uc758 \ub3c5\ub9bd \uadf8\ub8f9\uc758 \ud3c9\uade0 \uac04\uc5d0 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \ucc28\uc774\uac00 \uc788\ub294\uc9c0 \uc5ec\ubd80\ub97c \ud655\uc778\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 R\uc5d0\uc11c \uc591\ubc29\ud5a5 ANOVA\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. \uc608: R\uc758 \uc591\ubc29\ud5a5 ANOVA \uc6b4\ub3d9 \uac15\ub3c4\uc640 \uc131\ubcc4\uc774 \uccb4\uc911 \uac10\ub7c9\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294\uc9c0 \ud655\uc778\ud558\uace0 \uc2f6\ub2e4\uace0 \uac00\uc815\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774 \uacbd\uc6b0 \uc6b0\ub9ac\uac00 \ubcf4\uace0 \uc788\ub294 \ub450 \uac00\uc9c0 \uc694\uc18c\ub294 \uc6b4\ub3d9 \uacfc […]<\/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-485","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nR\uc5d0\uc11c \uc591\ubc29\ud5a5 ANOVA\ub97c \uc218\ud589\ud558\ub294 \ubc29\ubc95 - 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