(x - r(min)) / (r(max) - r(min))
does not yield missing when r(min) is 0 unless x is missing or r(max) is
also zero. But that's neither here or there. The above is just a linear
rescaling of a variable and will thus leave skewness unchanged.
Skewness of a regressor is not itself fatal to anything.
Censoring of a regressor is something to take account of in
interpretation. If you regard such a regressor as error-free, as one
usually does, then I am not clear that procedure need otherwise be
affected.
Nick
[email protected]
mbaier
I tried to transform it according to ln(skewed variable), but my
regressor has a lots of values at zero, for which ln is not defined. I
also tried to create an index like I=100*(x-r(min))/(r(max)-r(min)),
which again leads to many missings (due to many x's being zero).
What can I do?
Besides, do I have to account for the censoring of my regressor? If so,
what can I do?
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