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We can simply normalize data by subtracting it from mean & dividing that term by standard deviation.
I hope, it will be useful to you.
I have got your concern. Could you please share sample data on which normalization needed. I'll also try from my end & will share you the approach.
If I am correct, your main objective is to transform non-normal data to normal. You can use Johnson transformation.
I hope, it will help you.
I'd like to help with your question but want to get some background information first. I assume you are trying to satisfy the requirement of normality of errors in your regression model?
How are you determining that your independent variables are skewed? What measure or method are you using?
Try plotting the residuals of the independent variable and the dependent variable and see what the pattern or shape looks like. That can help you determine what transformation you need to use on your data. Normalizing your data wont hurt either, it will give you more robust coefficient estimates but will change the interpretation slightly.
Let us know how it works!
Box Cox transformation, apply boxcox to see the closest transformation power you can apply to make the relationship linear, if first difference and log normalization did not work.
I would suggest bootstrap and/or jackknife methods (in general, resampling is useful in highly non-normal data)
I would have taken the approached Mortal Kolle mentioned. However my starting point would also have been Box Cox, but as you mentioned this was unsuccessful.