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


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?

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