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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

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. Nick n.j.cox@durham.ac.uk 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? * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: interpreting R-squared when constant has been supressed***From:*Lloyd Dumont <lloyddumont@yahoo.com>

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