Assume that I have a variable whose distribution is skewed positively to a very high degree, such that taking the log will not be sufficient in order to bring it within the range of skewness for a normal distribution. What are my options at this point? What can I do to transform the variable to a normal distribution?

**Answer**

Try straight Box-Cox transform as per Box, G. E. P. and Cox, D. R. (1964), “An Analysis of Transformations,” *Journal of the Royal Statistical Society, Series B*, 26, 211–234. SAS has the description of its loglikelihood function in Normalizing Transformations, which you can use to find the optimal λ parameter, which is described in Atkinson, A. C. (1985), *Plots, Transformations, and Regression*, New York: Oxford University Press.

It’s very easy to implement it having the LL function, or if you have a stat package like SAS or MATLAB use their commands: it’s *boxcox* command in MATLAB and PROC TRANSREG in SAS.

Also, in R this is in the MASS package, function boxcox().

**Attribution***Source : Link , Question Author : histelheim , Answer Author : kjetil b halvorsen*