I had a question that involves the basic assumptions of t-tests and anova models. I know there are assumptions of normality but wasn't sure where they need to be applied to.
I see most people explain that the normality assumption is related to the residuals and the standard way to see this is to make a qqplot with the residuals.
Could the normality assumption also or instead be applied to the predictors? For example, creating a qqplot for the data for each group in an anova, or would that not matter?
I wrote a quick example to show what I'm talking about...
dat<- data.frame(Class=c(rep("A", 5), rep("B", 5), rep("C", 5)), Value=rnorm(15)) #now does it make sense if i do this for each class #or does it need to be the model residuals qqnorm(subset(dat, dat$Class=="A")$Value) qqline(subset(dat, dat$Class=="A")$Value)