Chapter 14 Variables transformations

So far we have discussed linear regression models, where the response variable linearly depends on a set of explanatory variables. These models work well in many contexts, especially when the response variable is measured in high volumes (e.g. sales in thousands of units). However, in reality the relations between variables can be non-linear. In this chapter we consider an example of application to see how transformations can be motivated by a real life example and then discuss different types of transformations and what they imply for analytics and forecasting

The software behind this book. The methods described here are implemented in the greybox package for R and Python, which are free and open source.

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