Dear Brendan,
Because it is a logistic and not probit regression you are attempting,
it actually does not matter if your variables are normal or not. The
main assumption that you might want to test is that the relationship
between the logit of your outcome and your predictor variables is linear
and that all the relevant predictors are included
linktest is a basic way of testing this- the predicted variable (_hat)
should be significant while its square (hatsq) should not be- if you
have specified the right link and variables. But if the box-tidwell test
tells you the same thing I wouldn't worry about the normality issue.
Cheers,
Seyi
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Brendan
Sent: 27 November 2007 11:05
To: Stata List
Subject: st: Testing normality of a continuous predictor variable in a
logistic model
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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