Re: st: multiple regression, r squared and normality of residuals

[David Greenberg <dg4@nyu.edu>]

When you tranform your dependent variable, the R-squared values of the different models are no longer comparable, so this comparison should not play a role in your choice of models. David Greenberg, Sociology Department, New York University

----- Original Message -----
From: Arti Pandey <rtpandey@yahoo.com>
Date: Tuesday, March 22, 2011 9:13 pm
Subject: st: multiple regression, r squared and normality of residuals
To: statalist@hsphsun2.harvard.edu


> Hello
> 
> I ran multiple regression with in stata using two models;
> the first gave an R-squared of .35, p values of all predictors was 
> less than 
> 0.001 except one which was less than 0.05. No.  of obs. used was 84, 
> distribution of residuals was normal.
> Then I did a log transform of the dependent variable, r squared went 
> up to .65, 
> p values for all predictors was 0.001 except the one mentioned above, 
> which is 
> now 0.06. The residuals were also slightly skewed to the left. No. of 
> obs went 
> down to 77.
> My question is how do I decide between the R squared and distribution 
> of 
> residuals. Is such a high rise in R squared worth sacrificing no of 
> observations 
> 
> and normal distribution of residuals for. Since the skew is not very 
> pronounced, 
> 
> it is tempting to go with the second, but then the regression  model 
> might be 
> wrong.....
> Appreciate any help.
> Arti
> 
> 
>       
> 
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