# What is the ideal distribution for a GLM for comparing survey data with election results?

I'm relatively new to statistical methods. I'm hoping to learn what kind of distribution I should use for some data I have.

My dependent variable is the results a candidate received by county. My independent variable is the labor participation rate of each county (a pct).

I am wondering what the best distribution to use is when constructing a GLM.

It seems to me like the gamma or normal distributions are ideal. Any further suggestions for where I can learn to build models for this kind of data are appreciated, either as books or papers or anything.

• 1. What does "results" mean, specifically? What does the variable consist of? 2. labor participation rate is a percentage? 3. Why are you predicting labor participation as a function of candidate results -- I imagine most people would expect the relationship to be formulated with results being predicted from labor participation rate May 29, 2016 at 7:52
• Ok so clearly have the two switched. Will amend. Thought the participation depended on the vote. And yes it's a pct. May 29, 2016 at 12:44
• If you think the vote causes participation, why would other peoples' expectation matter? (unless this is an exercise for a class or something?) May 29, 2016 at 22:04
• I'm just self-teaching. I suppose I had some other way of thinking about it, which in retrospect doesn't make much sense. May 29, 2016 at 22:05
• You still haven't clarified how "results" is measured, which is important if it is to be your DV. If participation were to be your response I'd be inclined to model something like labor participation as beta, but if the mean doesn't change a lot you might be able to treat it as normal, say. Another alternative that would at least deal with a beta-like mean-variance relation would be to teat it as quasibinomial. Beta regression is not likely to be included in GLM in most stats packages, but may offered separately May 29, 2016 at 22:12