If your variables are hopelessly non-normal, you should consider converting
them to categorical or dichotomous variables.
Otherwise, you can transform your variables by changing the sign to reverse
the direction of skewness and substracting (mininum-1) to make them
strictly positive. Thus, if Y has negative skewness, -Y has positive skewness;
and if X takes negative values, X-(min(X)-1) will be strictly positive.
hth,
Jeph
[email protected] wrote:
Hello.
I need following help. I have panel dataset for estimating a simple linear
equation.
The problem is that my all variables have sknewness and big variation(large
std).
In particualr, the dependent variable and one of independant variables have a
negative sknewness, while all other independant variables are shown by positive
sknewness. My first intension is using a log transformation of all variables but
seems not to be a good idea since all variables have negative values (around
20%)
Besides, all variables except one of independant variables are ratio, thus that
idea would make worse.
I would be so glad if anyone has suggestions to solve this problem
THANKS
WT
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