@mthorne - what to do with zeroes? Well, the beta distribution can't be fit, as it is only supported on the open interval (0,1). Binomial should be fine, as it is defined on the closed interval [0,1]. Everything depends on the data generating process - if you are counting things or counting events out of trials, you should explore Poisson/negative binomial in the first case and binomial in the second. If you are measuring something, normal and lognormal are logical starting points. If you are measuring a ratio of continuous variables, beta is good. If you are modeling waiting times, a gamma distribution seems appropriate.
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To be honest I use GLM for two things - multivariate analysis for a designed experiment and examining heterogeneity of variance. I have been using PROC MIXED for over 30 years now, so it seems second nature to me. I want the REML estimates rather than the OLS estimates. I want to infer to larger inference spaces than inferring to repeating the identical experiment on identical components - down that path lies the issue of replicability, as the experiments and components are never truly identical.
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SteveDenham
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