Re: st: RE: Rituals [was: Terminology for supposedly all-purpose summaries]

[Richard Williams <Richard.A.Williams.5@nd.edu>]

At 10:37 AM 10/11/2004 -0400, Stas Kolenikov wrote:
> Also, I've seen people deriving F-statistics for testing an increase
> in R2 as you add variables to the model. So it is the R2 that is the
> "first principle" for them. The distinction between "measures" and
If by that you mean researchers are primarily interested in the R^2, I 
wouldn't agree (at least not always).  If I have X1, X2, and X3 in the 
(constrained) model, and I then add X4, X5, and X6 to the (unconstrained) 
model, I'm likely interested in testing the hypothesis

H0: B4 = B5 = B6 = 0
HA: At least one of the above betas does not equal 0.

If the null is rejected, one consequence will be that R^2 will be 
significantly larger in the unconstrained model.  I may use a formula that 
uses the R^2s in the calculations of the F statistic, but that does not 
mean I am focused on R^2 or have any interest in it other than as a piece 
of information needed to compute what I really want.  I could use 
equivalent formulas using, say, the error sums of squares that give the 
same results.  In other words, I would say that I am primarily interested 
in whether any of the added variables have nonzero effects, and the change 
in R^2 is simply a byproduct that I may or may not choose to focus on.  I 
discuss this on pp. 6-7 of

http://www.nd.edu/~rwilliam/stats1/x93.pdf

What I'm describing is (what I call anyway) an incremental F test.  In 
Stata, you could get an equivalent result by using the -test- command, 
which does a Wald test.

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