Differences

This shows you the differences between two versions of the page.

--- anova [2020/05/20 15:36] – [Example] hkimscil
+++ anova [2020/05/20 15:43] – [Example 2] hkimscil
@@ Line 580: / Line 580: @@
 F<sub>cal</sub> > F<sub>crit</sub> 이므로 3집단 간의 평균은 통계학적으로 의미가 있는 차이를 가지고 있다.
 ====== Example 2 ======
-<code>x1 <- c(15, 20, 14, 13, 18, 16, 13, 12, 18, 41)
+<code>
+x1 <- c(15, 20, 14, 13, 18, 16, 13, 12, 18, 41)
 x2 <- c(21, 25, 29, 18, 26, 22, 26, 24, 28, 21)
 x3 <- c(28, 30, 32, 28, 26, 30, 25, 36, 20, 15)
 </code>
-<code>> data.frame(x1,x2,x3)
+<code>> xc <- data.frame(x1,x2,x3)
    x1 x2 x3
   15 21 28
@@ Line 634: / Line 635: @@
 <code>
-> colnames(xs[1]) <- "wrong"
+> colnames(xs)  <- c("wrong", "condition")
-> colnames(xs[2]) <- "cond"
 </code>
-<code># cf
+<code>
+# cf
 # lengthofelements <- length(x1)
 # varofvariable <- var(x1)</code>
@@ Line 651: / Line 652: @@
 ss_x3
-df_bet
+ss_tot
-ss_bet
 df_tot
-ss_tot
+ss_with
 df_with
-ss_with
+ss_bet
 df_bet
-ss_bet
 </code>
 <code>
-df_tot <- length(xs$ind) - 1
+df_tot <- length(xs$wrong) - 1
-ss_tot <- var(xs$values)*df_tot
+ss_tot <- var(xs$wrong)*df_tot
-var_tot <- var(xs$values)
+var_tot <- var(xs$wrong)
 df_x1 <- length(x1)-1