What is better to transform doing linear regression, response or explanatory variable(s)? 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?)
 A: It depends on the situation. When you have multiple independent variables, sometimes only one of them has a nonlinear relationship - in this case, transforming the dependent variable may cause problems with the other variables. In some cases, a log transform makes more substantive sense for one or the other variable. If you log transform the DV only then you are saying that arithmetic changes in the IVs relate to geometric changes in the DV. If you transform (some or all) IVs, then just the reverse.  Often, variables related to income or other amounts make more sense log transformed. That is, a change in income from \$20,000 per year to \$40,000 is more like (in some sense) a change from \$200,000 to \$400,000 than a change from \$200,000 to \$220,000. If NONE of your variables can be sensibly log-transformed, it might be better to pursue some non-linear regression such as splines.  
A: Doing linear reg. its better to tranform the independent (explanatory ) variable and use the tranformation methods according to your data and cheak r-square for the model and MSE(mean square of error) for the modle if r-square is higher and MSE is mininmum you can say tranformation is appropriate..
