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--- anova [2018/10/18 08:34] – [논문 작성] hkimscil
+++ anova [2018/10/19 08:06] – [Post hoc test] hkimscil
@@ Line 375: / Line 375: @@
 ====== Post hoc test ======
 [[Post hoc test]]
+<code>> adata <- read.csv("https://datascienceplus.com/wp-content/uploads/2017/08/tyre.csv")
+> adata
+        Brands  Mileage
+       Apollo 32.99800
+       Apollo 36.43500
+       Apollo 32.77700
+       Apollo 37.63700
+       Apollo 36.30400
+       Apollo 35.91500
+       Apollo 34.70000
+       Apollo 32.37900
+       Apollo 33.63100
+      Apollo 36.41900
+      Apollo 36.43000
+      Apollo 34.83600
+      Apollo 38.32800
+      Apollo 30.62300
+      Apollo 32.57500
+Bridgestone 33.52300
+Bridgestone 31.99500
+Bridgestone 35.00600
+Bridgestone 27.87900
+Bridgestone 31.29700
+Bridgestone 31.06200
+Bridgestone 34.83800
+Bridgestone 33.97600
+Bridgestone 32.55200
+Bridgestone 30.88100
+Bridgestone 28.14400
+Bridgestone 29.18400
+Bridgestone 33.07500
+Bridgestone 32.36500
+Bridgestone 30.92500
+        CEAT 34.44565
+        CEAT 32.80658
+        CEAT 33.41499
+        CEAT 36.86118
+        CEAT 36.97277
+        CEAT 35.08145
+        CEAT 34.95412
+        CEAT 33.47516
+        CEAT 30.42748
+        CEAT 36.13392
+        CEAT 34.78336
+        CEAT 36.11675
+        CEAT 41.05000
+        CEAT 32.16845
+        CEAT 32.72624
+      Falken 39.59600
+      Falken 38.93700
+      Falken 36.12400
+      Falken 37.69500
+      Falken 36.58600
+      Falken 35.96700
+      Falken 36.73700
+      Falken 34.31000
+      Falken 40.25200
+      Falken 37.38200
+      Falken 40.66300
+      Falken 37.09500
+      Falken 38.00500
+      Falken 37.75600
+      Falken 37.26500
+>
+</code>
+<code>> fmod.tire <- aov(Mileage~Brands, data=adata)
+> summary(fmod.tire)
+            Df Sum Sq Mean Sq F value   Pr(>F)
+Brands       3  256.3   85.43   17.94 2.78e-08 ***
+Residuals   56  266.6    4.76
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+>
+</code>
+<code>
+> TukeyHSD(fmod.tire)
+  Tukey multiple comparisons of means
+% family-wise confidence level
+Fit: aov(formula = Mileage ~ Brands, data = adata)
+$Brands
+                          diff        lwr       upr     p adj
+Bridgestone-Apollo -3.01900000 -5.1288190 -0.909181 0.0020527
+CEAT-Apollo        -0.03792661 -2.1477456  2.071892 0.9999608
+Falken-Apollo       2.82553333  0.7157143  4.935352 0.0043198
+CEAT-Bridgestone    2.98107339  0.8712544  5.090892 0.0023806
+Falken-Bridgestone  5.84453333  3.7347143  7.954352 0.0000000
+Falken-CEAT         2.86345994  0.7536409  4.973279 0.0037424
+>
+> tapply(adata$Mileage, adata$Brands, mean)
+     Apollo Bridgestone        CEAT      Falken
+.79913    31.78013    34.76121    37.62467
+>
+</code>
 ====== F and t value ======
 $$ F = t^{2}$$
+<code>> td <- read.csv("D:/Users/Hyo/Cs-Kant/CS/Rdata/t-test.csv")
+> head(td)
+  gender tmobconv out in. mobpeo
+      2       60   3   3      5
+      2       60   5   3      5
+      1       20   3  10      5
+      2       10   7   5      6
+      2       30   3   3      3
+      2       30   5   5      3
+>
+</code>
+<code>
+> tmod <- t.test(tmobconv~gender, data=td, var.eq=T ,na.rm=T)
+> tmod
+	Two Sample t-test
+data:  tmobconv by gender
+t = -3.0453, df = 1449, p-value = 0.002367
+alternative hypothesis: true difference in means is not equal to 0
+percent confidence interval:
+ -48.88717 -10.58088
+sample estimates:
+mean in group 1 mean in group 2
+.28457       121.01860
+> tmod$statistic^2
+       t
+.273615
+</code>
+<code>
+> fmod <- aov(tmobconv~gender, data=td)
+> summary(fmod)
+              Df   Sum Sq Mean Sq F value  Pr(>F)
+gender         1   320284  320284   9.274 0.00237 **
+Residuals   1449 50044262   34537
+---
+Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+observations deleted due to missingness
+>
+</code>
 ====== Example ======