As I understand it, the skewness of the response variable in a linear regression does not need to be normal (only the residuals need to be normally distributed). However, I was generally wondering if I can log-transform my response variable so its distribution becomes more normal prior to regression and if this has any benefits/drawbacks.
For context, my response variable
y is Medicaid coverage, which has a right-skew in the state I am analyzing. If I log transform this variable using
np.log(y + 1), the distribution looks approximately normal. I was wondering if this would be appropriate for a linear regression, or if I don't even need to do this/it is bad practice to do this.