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Re: st: linear probability model (LPM)
I should have stated: For proportions in the range .2 to .8 linear,
logit, and probit models give similar predictions (Cox. Analysis of
Binary Data, 1970)
On Apr 26, 2007, at 11:52 AM, Steven Samuels wrote:
A linear probability model is desirable because effects are risk
differences, which are much easier to interpret than odds ratios.
It's best for proportions that are not too close to 0 or 1;
otherwise the model may predict probabilities outside those
boundaries. (In this range linear, probit, and logit models give
similar predictions-Cox, Analysis of Binary Data, 1972).
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