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

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

Subject |
RE: st: logistic tranformation, proportion variables |

Date |
Fri, 14 Dec 2007 12:48:27 -0000 |

As long as you don't have major outliers already or create such by a unwise transformation -- and clearly I advise against that -- the shape of the marginal distribution of a predictor is likely to be a much smaller deal than whether the relationship between the response and the predictor, conditional on other variables, is linear or not. Reducing a nonlinear relationship to something more nearly linear is then the main motive for a transformation. As pointed out more than once in this thread, powers such as square roots or squares would deal smoothly with zeros. Which flavour you want depends on your data, which we can't see. I am not clear that any kind of folded transformation is natural for a predictor, bounded or not. Marck Bulter I totally agree, conversion is an awfull solution, fitting the data to the model. But still I have to do something with the heteroskedacity and the non normal resid's. As suggested in your transit files, I will give folded transformation a try. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: logistic tranformation, proportion variables***From:*Marck Bulter <177316mb@student.eur.nl>

**Re: st: logistic tranformation, proportion variables***From:*David Airey <david.airey@Vanderbilt.Edu>

**RE: st: logistic tranformation, proportion variables***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**Re: st: logistic tranformation, proportion variables***From:*Marck Bulter <177316mb@student.eur.nl>

**Re: st: logistic tranformation, proportion variables***From:*"Austin Nichols" <austinnichols@gmail.com>

**Re: st: logistic tranformation, proportion variables***From:*Marck Bulter <177316mb@student.eur.nl>

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