You should review your course material on interpreting general linear
models. The criterion you have chosen for "significance" (looking at p
values for indivdiual coefficients) is not the recommended one. Seek
out the section that discusses the proper method for using deviance
estimates for comparing nested models.
--
David Winsemius
On Apr 21, 2009, at 4:32 AM, ehud cohen wrote:
Hi,
We have an experiment with pass/fail outcome, and a continuous
parameter which may contribute to the outcome.
First, we've analyzed it by:
p=c(F,T,F,F,F,T,T,T,T,T,T,T,F,T,T,T,T);
w=c(53,67,59,59,53,89,72,56,65,63,62,58,59,72,61,68,63);
l<-glm(p~w,family=binomial)
summary(l)
Which turned out to be non significant.
Then, we thought of comparing the parameters of the two groups (passed
vs. failed)
t.test(w[which(p)],w[which(!p)],alternative="two.sided")
which turned highly significant.
I'd appreciate some insight...
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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