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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: tobit? |

Date |
Tue, 12 Aug 2008 14:06:19 +0100 |

BMI I take to be body-mass index. SES is perhaps socio-economic status, but I have no idea how it is measured. It's always helpful to have in mind that most people on Statalist will not work in your own field and may not know whatever jargon is standard to you. Generalising your question somewhat to a search for some smooth(BMI | SES), possibly a linear function of SES, it is standard that at most any assumption of normality is about conditional distributions for BMI given SES, not marginal distributions. Also, a transformation does not have to "work" perfectly (whatever that means) to be useful. Note that ln and log10 are going to have exactly the same effect on the shape of a distribution, as they differ by only a multiplicative constant. -tobit- is for censored responses; I do not see any indication here that that is your situation. If I had your data I would want to try out various smooths before working out the best modelling method. Fractional polynomials are particularly well supported in Stata. Nick n.j.cox@durham.ac.uk Mona Mowafi 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? * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: tobit?***From:*"Mona Mowafi" <mmowafi@hsph.harvard.edu>

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