Keep in mind that you need normality of the conditional distribution
(residuals) rather than the marginal distribution (BMI). May be
skewness in your demographics will explain the skewness in BMI, so run
your regression of interest and then see how the residuals look like.
If you are still not happy,
1. try Box-Cox transformation, -help boxcox-.
2. try robust regression, -help rreg-
3. find a slimmer population to work with :))
On Tue, Aug 12, 2008 at 7:42 AM, Mona Mowafi <[email protected]> wrote:
> Dear statalisters,
>
> I have a dataset in which I am evaluating the effect of SES on BMI and BMI is heavily skewed toward obesity (i.e. over 50% of the sample >30 BMI). I preferred to run a linear regression so as to use the full range of data, but the outcome distribution violates normality assumption and I've tried ln, log10, and sqrt transformations, none of which work.
>
> Is it appropriate to use tobit for modeling BMI in this instance? If not, any suggestions?
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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