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

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

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

Date |
Tue, 27 Nov 2007 12:03:52 -0000 |

Maarten has already made what I think is by far the most important point, that marginal normality (Gaussianity) of predictors is not an issue. I want to comment on a detail. Whether a histogram or a cumulative frequency curve "looks normal" is in my view very difficult to judge reliably. In the case of a histogram there are decisions over bin width and bin origin that are necessarily arbitrary. Even if a Gaussian density or distribution function is superimposed, as the case may be, comparison is still problematic. More positively, a normal plot [quantile-quantile plot, presumably] is customised for this problem and far more useful. An alternative test for normality is given by -omninorm- on SSC. I don't use myself much, but it was fun to program. Brendan ------- 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? * * 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: Testing normality of a continuous predictor variable in a logistic model***From:*Brendan <hsct@icon.co.za>

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