Others have commented. In my view, the only useful
R-square here is likely to be the square of the
correlation between observed and fitted.
Nick
[email protected]
Lloyd Dumont
> Hello. I am running an OLS model in which
> observations fall into one of three mutually-exclusive
> and collectively-exhaustive categories. For clarity
> in reporting, I thought it would be a good idea to
> suppress the constant and report slope estimates for
> all three dummies.
>
> If I run the model both ways (either with two dummies
> and the constant vs. with all three dummies and no
> constant), the estimates and the standard errors are
> what they should be, i.e., are the same in relative
> terms to one another in both models, same t-stats,
> etc. But, without the constant, the R2 shoots up from
> something like .11 to something like .68.
>
> I sort of understand conceptually how this could
> happen--fit is now relative to zero than to the mean.
> But...
>
> 1. Is my understanding correct?
> 2. How can I explain this succinctly?
> 3. Am I being deceptive to report the .68?
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