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st: RE: RE:xtpcse vs. xtpcse, noconstant


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE:xtpcse vs. xtpcse, noconstant
Date   Tue, 10 Aug 2004 12:07:32 +0100

Sounds risky to me. 

As said, the R^2 is just not a guide to model 
merit in this case. Otherwise I clearly can't 
comment on your results, but I'd advise seeking out 

e.g. 

John A. Nelder. 1998. 
The Selection of Terms in Response-Surface Models
-How Strong is the Weak-Heredity Principle?
The American Statistician 52(4): 15-318.

and following references from there. 

Nick 
n.j.cox@durham.ac.uk 

carambas
> 
> Thank you, Nick.
> 
>   Mine is a yield response model and since I am including 
> mostly inputs as
> explanatory vars and some categorical dummies, so perhaps a 
> model without
> const could work since no matter what I include, I get only 
> good results
> with noconst.
> 
> Cris
> 
> 
> Date: Mon, 9 Aug 2004 09:14:27 +0100
> From: "Nick Cox" <n.j.cox@durham.ac.uk>
> Subject: st: RE:
> On the R^2, your starting point is now a prediction of zero,
> not a prediction of the mean response.
> 
> In a much simpler case, below,  dropping the constant
> gives a higher R-sq but a totally ludicrous model. Why then
> does the R-sq look so good? Because the predictions
> - -- which range from 11 to 30 mpg -- are much closer to
> the data than a prediction of 0 than the predictions of
> the first model to the mean of -mpg-. Your model is more
> complicated, and I can't see your data, but I guess that
> the same applies. If there is a really good reason,
> like a law of physics, to force predictions through
> the origin, then do it. (One can certainly improve
> on a linear regression of -mpg- on -weight-, a secondary
> point.)
> 

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