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I have a bunch of independent variables: height, weight, etc that I want to regress a dummy variable on to. For instance, if I wanted to regress diabetes (0 if patient doesnt have diabetes, 1 if patient does have diabetes) and I wanted to figure out the effect of an increase in 1 pound of weight on the probability of having diabetes, how would I do that? I'm sure there are multiple ways of doing it but I just never have heard of a model that does this. I thought it was the probit model but I'm not sure. Any thoughts?

Pcarlitz
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The problem you are describing is known as logistic regression; a web search for that should turn up a lot of hits. Most commonly, the response is some function of a linear combination of inputs, but more generally, the response could be a nonlinear function of inputs.

The dependence of the response on an input (e.g. weight) is interesting, but not exactly well-posed, since the change of the probability of the response varies over the range of the input variable; the change is very small for very large or very small values of the input, and reaches some maximum in between.

Robert Dodier
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  • so i should use a logit model then? above is just an example. im not actually using diabetes as my dependent. will the coefficients of that model give me probabilities? – Pcarlitz Jan 09 '14 at 20:37
  • @Pcarlitz yes, logit is worth considering, so is probit; there are other variations on the idea. The coefficients of a logit model tell the slope of log(p/(1 - p)) (you can confirm that yourself); that makes it a little bit easier to explain, although not much. – Robert Dodier Jan 09 '14 at 22:17
  • ok great. do you know of anyway I can convert the coefficients to probabilities easily using stata? – Pcarlitz Jan 10 '14 at 23:56
  • @Pcarlitz sorry, I don't know anything about Stata. – Robert Dodier Jan 11 '14 at 01:05