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st: RE: interpreting R-squared when constant has been supressed

From   "Nick Cox" <[email protected]>
To   <[email protected]>
Subject   st: RE: interpreting R-squared when constant has been supressed
Date   Fri, 28 Sep 2007 15:49:01 +0100

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. 

[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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