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... Thanks, Ehud.
Question on binomial data
2 messages · ehud cohen, David Winsemius
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