I have a lognormally distributed continuous dependent variable that I would like to predict using multiple regression. I am using a forward selection process and have selected three predictor variables at the moment:
- x1: A continuous variable with approximately normal distribution and slight negative skew
- x2: A continuous variable with normal distribution
- x3: A categorical variable with 14 categories.
If it helps, here are some plots:
I have two questions:
- Should I log-transform the dependent variable to make it normally distributed before feeding into a regression?
- With or without the transformation, what are some basic rules of thumb for determining what kind of (multiple) regression to use?
Some extra points:
- There is no pairwise collinearity between the predictor variables.