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

[Keith Dear <keithdear4@gmail.com>]

You could also try a sqrt transform, since the log seems to have
overcooked it: see -ladder-. And you appear to have seven zeros (or
negatives) which is why you are losing N: a common solution is to use
log(1+x); or try -lnskew-

However since your original residuals appeared normal, why are you
transforming the dependent variable at all? You probably have
something other than a straight-line dependence on one or more
covariates, so should be concentrating on the RHS of the equation not
the left. -fracpoly- and -mfp- may help, or or just lots of
scatterplots. Also see -help regress postestimation- for -avplot- and
others.

Keith

On 23 March 2011 12:11, Arti Pandey <rtpandey@yahoo.com> wrote:
>
> 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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>



-- 
Dr Keith Dear
National Centre for Epidemiology and Population Health
ANU College of Medicine, Biology and Environment
Australian National University
Canberra, ACT 0200 Australia
CRICOS provider #00120C
Phone +61 (02) 6273 2208
Mobile 0424 450 396

*
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