# What transformation should be used when different transformations are required for each predictor?

I am familiar with exponential, quadratic, power etc. transformations of variables in univariate regression when necessary based on non-normality in the relationship between the independent and dependent variable, such as skewness. My question is this: If, after reviewing each relationship via scatterplot between a dependent variable and multiple independent variables, it is determined that multiple and different transformations are needed, how does one determine the equation needed to transform the predictors? For instance, a reciprocal model may seem to fit best when comparing the DV to IV_1 and a logarithmic model when comparing DV to IV_2, but each transformation requires a different equation to back transform.

• It's a great question, but to do justice to it requires a textbook. A very abbreviated exposition, focusing on practical, exploratory methods, is available on my Web site at quantdec.com/misc/MAT8406/Meeting07 : link to the "diagnostic plots" PDF file. In 24 heavily illustrated pages (with plenty of working R code) I provide a principled, quantitative, and effective workflow for addressing these problems. It is addressed to people learning about multiple regression at the upper undergraduate to beginning graduate level, but using minimal mathematical background.
– whuber
Mar 16, 2016 at 20:36
• Thank you, whuber. I have added a follow up question in the comment under Data Science Dojo's answer, if you would like to chime in there as well. Mar 18, 2016 at 15:20
• If your aim's to build a predictive model on the data, setting aside some of them for this kind of exploratory analysis is a good idea. Another approach is to use polynomial or spline bases for the predictors, allowing approximation of even non-monotonic curvilinear relationships. Mar 18, 2016 at 15:52
• Thanks, @Scortchi, on both accounts. I am trying to avoid a polynomial or spline-based regression because I am unable to interpret the models as clearly is if it were a standard linear regression with transformed predictors. I actually ran into this problem recently as I could not interpret the GAM I had used, which used thin-plate regression, outside of looking at the multiple R of the model. It also had poor predictive power despite having good descriptive power. If you have any insight into the follow up question I posed in a comment to Data Science Dojo, I would be very grateful. Mar 18, 2016 at 16:07

For example, if I have two IVs $x_1$ and $x_2$ and a DV $y$, where $x_1$ has an exponential relationship to y (i.e. $e^{x_1}~\alpha~y$) and $x_2$ has a quadratic relationship to y (i.e. $x_2^2~\alpha~y$), then I will create two new IVs $x_1'$ and $x_2'$ such that $x_1' = e^{x_1}$ and $x_2' = x_2^2$. These new IVs have a linear relationship to $y$ by definition, allowing me to use a simple linear regression model. To extract useful coefficients from the linear regression, simply apply the inverse transformation (ln for $x_1'$ and square root for $x_2'$).