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--- c:ms:2025:w07_anova_note [2025/04/10 07:12] – hkimscil
+++ c:ms:2025:w07_anova_note [2025/04/16 01:40] (current) – [R square: output] hkimscil
@@ Line 772: / Line 772: @@
 ></code>
+{{:c:ms:2025:pasted:20250414-084011.png}}
+{{:c:ms:2025:pasted:20250414-084020.png}}
+{{:c:ms:2025:pasted:20250414-084030.png}}
 ====== Post hoc test ======
 [[:post hoc test]]
@@ Line 829: / Line 832: @@
 # 이제 위의 점수를 .05 레벨에서 비교할 점수를 찾아 비교한다
 # qtukey 펑션을 이용한다
-t.crit <- qtukey( .95, nmeans = 3, df = 45)
+t.crit <- qtukey( .95, nmeans = 4, df = 30 * 4)
 t.crit
@@ Line 934: / Line 937: @@
 > # 이제 위의 점수를 .05 레벨에서 비교할 점수를 찾아 비교한다
 > # qtukey 펑션을 이용한다
-> t.crit <- qtukey( .95, nmeans = 3, df = 45)
+> t.crit <- qtukey( .95, nmeans = 4, df = 30 * 4)
 > t.crit
-[1] 3.427507
+[1] 3.684589
 >
 > # 혹은 R이 보통 제시한 거꾸로 방법으로 보면
@@ Line 1026: / Line 1029: @@
 <code>
 > ss.total
-[1] 4842.5
+[1] 3900
 > ss.between
-[1] 682.5
+[1] 300
 > r.sq <- ss.between / ss.total
 > r.sq
-[1] 0.1409396
+[1] 0.07692308
 >
 > # then . . . .
@@ Line 1043: / Line 1046: @@
 Residuals:
      Min       1Q   Median       3Q      Max
--14.8928  -4.3342  -0.3732   4.4517  13.0126
+-13.1181  -3.9308  -0.3568   3.9491  11.7704
 Coefficients:
             Estimate Std. Error t value Pr(>|t|)
-(Intercept)   26.000      1.093  23.780  < 2e-16 ***
+(Intercept)   26.000      1.017  25.563  < 2e-16 ***
-groupB        -1.000      1.546  -0.647 0.519080
+groupB        -1.000      1.438  -0.695  0.48831
-groupC        -4.000      1.546  -2.587 0.010919 *
+groupC        -3.000      1.438  -2.086  0.03920 *
-groupD        -6.000      1.546  -3.880 0.000174 ***
+groupD        -4.000      1.438  -2.781  0.00633 **
 ---
 Signif. codes:
 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-Residual standard error: 5.988 on 116 degrees of freedom
+Residual standard error: 5.571 on 116 degrees of freedom
-Multiple R-squared:  0.1409,	Adjusted R-squared:  0.1187
+Multiple R-squared:  0.07692,	Adjusted R-squared:  0.05305
-F-statistic: 6.344 on 3 and 116 DF,  p-value: 0.0005079
+F-statistic: 3.222 on 3 and 116 DF,  p-value: 0.02527
 > anova(lm.res)
@@ Line 1063: / Line 1066: @@
 Response: values
-           Df Sum Sq Mean Sq F value    Pr(>F)
+           Df Sum Sq Mean Sq F value  Pr(>F)
-group       3  682.5 227.500  6.3437 0.0005079 ***
+group       3    300 100.000  3.2222 0.02527 *
-Residuals 116 4160.0  35.862
+Residuals 116   3600  31.034
 ---
 Signif. codes:
 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 >
->
 </code>