Hi,
I have found quite a few posts on normality checking of response variables, but
I am still in doubt about that. As it is easy to understand I'm not a
statistician so be patient please.
I want to estimate the possible effects of some predictors on my response
variable that is nº of males and nº of females (cbind(males,females)), so, it
would be:
fullmodel<-glm(cbind(males,females)~pred1+pred2+pred3, binomial)
I have n= 11 (ecological data, small sample size is a a frequent problem!).
Someone told me that I have to check for normality of the predictors (and in
case transform to reach normality) but I am in doubt about the fact that a
normality test can be very informative with such a small sample size.
Also, I have read that a normality test (Shapiro, Kolmogornov, Durbin, etc.)
can't tell you anything about the fact that the distribution is normal but just
that there is no evidence for non-normality.
Anyway, I am still looking for some sort of thumb of rule to be used in these
cases.
The question: is there some simple advice on the way one should proceed in this
cases to be reasonably confident of the results?
Thanks for any help you might provide
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