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?
Thank you. Lloyd Dumont
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