Maybe it is a very basic question and already answered, but I could not find a clear answer.
My plots of response vs. predictors show "curved" relationship, and log-transformation can help to achieve linearity. But it helps either when I log-transform response variable or when I log-transform explanatory variables, as well. The distributions of them all are more or less close to normal.
So, what is better to transform, response or predictors, from statistical point of view? Or it doesn't matter for regression and I only need to seek to normal distributions?
(I see here that when predictors are not transformed then R2 is related to the variance of the residuals and can be trusted. Is it the only reason?)