[injury log]

Originally Posted by Elendil

Well, those tables show simple regressions of the kind y=a+bx, where "y" is "this year's stat," "a" is the constant, "b" is a coefficient, and "x" is "last year's stat." So after I run those regressions for the MLB sample, I run the same regressions on the OOTP sample, and after each one I do a Hausman test to determine whether the coefficient ("b") in each OOTP-sample regression is equal to the coefficient in the corresponding MLB-sample regression. (Hope that's clear enough.)

So the Hausman test itself is actually quite simple. You take the coefficient and its standard error and use a t-statistic to determine whether you can reject, at a particular level of confidence, the null hypothesis that the coefficient is equal to some number (in this case, the coefficient derived from the equivalent MLB-sample regression). So a Hausman test is really just a generalization of the more usual test of statistical significance done in a regression, i.e., that b=0.

Thanks very much- I appreciate the explanation. It seems clear, but I need some coffee before I get into the details!