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From |
Richard Goldstein <richgold@ix.netcom.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: interpreting R-squared when constant has been supressed |

Date |
Thu, 27 Sep 2007 08:17:24 -0400 |

if you are using all 3 dummies, then use the "hascons" option and you will have directly comparable R-squared values

Lloyd Dumont wrote:

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

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