Normality of the predictors doesn't belong to the assumptions of the GLM,
so you don't have to check this.
Note, however, that there are all kinds of potential problems which to
detect is fairly hopeless with n=11 and three predictors, so you shouldn't
be too confident about your results anyway.
Christian
On Fri, 15 Apr 2011, Simone Santoro wrote:
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 n? of males and n? 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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*** --- ***
Christian Hennig
University College London, Department of Statistical Science
Gower St., London WC1E 6BT, phone +44 207 679 1698
chr...@stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche
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