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From |
Brendan <hsct@icon.co.za> |

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

Subject |
st: Testing normality of a continuous predictor variable in a logistic model |

Date |
Tue, 27 Nov 2007 13:04:49 +0200 |

I am working with a dataset containing 30000 observations. Some of the explanatory variables are continuous. If I perform usual tests for normality the numbers are too great for swilk or for sfrancia, and if I use sktest the result is "absurdly" large values and rejects the hypothesis of normal distribution. The frequency histogram, cumulative frequency plot and normal plot all look normal with no outliers. I presume that with such large numbers even very small deviations from normal will lead to a significant result. The box- tidwell test indicates that the model relationship is linear for all these continuous variables. Is it safe to ignore the sktest results?

Regards

Brendan

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**Follow-Ups**:**st: RE: Testing normality of a continuous predictor variable in a logistic model***From:*"Soremekun, Seyi" <S.Soremekun@warwick.ac.uk>

**st: RE: Testing normality of a continuous predictor variable in a logistic model***From:*"Nick Cox" <n.j.cox@durham.ac.uk>

**Re: st: Testing normality of a continuous predictor variable in a logistic model***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**st: R: Testing normality of a continuous predictor variable in a logistic model***From:*"Carlo Lazzaro" <carlo.lazzaro@tin.it>

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