proper robin
In a data analysis lesson entitled Randomized complete block design there's the following and I don't understand why increasing the F value increase the related probability. I thought the F value was a static curve a bit higher than the horizontal line, and the probability was that the F value the ANOVA yield is smaller than the actual F curve. If I'm understanding it right, wouldn't the higher the ANOVA yield is, the lower the chance it's smaller the actual F curve?
Would someone kindly explain?
Thank you
aov1.log <- aov(log(Masse) ~ Diete, data = cochons)
summary(aov1.log)
Df Sum Sq Mean Sq F value Pr(>F)Diete 3 2.2515 0.7505 43.12 6.83e-08 ***
Residuals 16 0.2784 0.0174Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
bloc.aov.log <- aov(log(Masse) ~ Diete + Bloc, data = cochons)
summary(bloc.aov.log)
Df Sum Sq Mean Sq F value Pr(>F)Diete 3 2.2515 0.7505 49.363 5.03e-07 ***
Bloc 4 0.0960 0.0240 1.579 0.243
Residuals 12 0.1824 0.0152Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
crude night