For Univariate GLM, You have to compute it yourself. For AIC, you need N, k (the number of parameters fit by the model, including the intercept), and RSS (the residual sum of squares). For BIC, things are a bit more complicated; so unless you really want it for some reason, I'd stick with AIC (or the second order corrected version for smaller samples).
http://en.wikipedia.org/wiki/Akaike_information_criterion
http://en.wikipedia.org/wiki/Bayesian_information_criterion
This reminds me of something I've been meaning to suggest to the good folks at SPSS: I think that AIC (and corrected AIC) would be very nice additions to the CURVEFIT procedure.
Bruce Weaver Professor, Lakehead University Canada
|