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I am working on my thesis analysis, and I have some error data that's right-skewed. I log-transformed it and ran glm on it (gaussian, identity in R) weighted by sample size, and my data is still over-dispersed. I keep seeing comments about using the quasi family, but I'm not sure I completely understand what I'm doing. I tried it out, and my question is, can I use quasi with the log-transformed data and still set my variance equal to my mean (or mean squared), or is that like a double variance stabilizer? If I run the data with quasi, link identity, variance = mean in R, I get approximately the same results, except the data appears to by neither under nor over-dispersed.

Thanks!

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Can you provide more details about your situation? What kind of data do you have? What does it mean to have over-dispersion if your response distribution is Gaussian? In general, I would not use the term "generalized linear model" to describe a model w/ an identity link & a Gaussian response (although it isn't wrong), I'd just say 'regression'. –  gung Nov 23 '12 at 23:36
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Erm... how is a gaussian 'over-dispersed'? Relative to what? –  Glen_b Nov 24 '12 at 0:33
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