# 16: Transformations


In this chapter, we focus on the fact that many statistical procedures work best if individual variables have certain properties. The measurement scale of a variable should be part of the data preparation effort. For example, the correlation coefficient does not require that the variables have a normal shape, but often relationships can be made clearer by re-expressing the variables.

• 16.1: Prelude to Transformations
The introductory chapter covered linear transformations. These transformations normally do not change statistics such as Pearson's r, although they do affect the mean and standard deviation. The first section here is on log transformations which are useful to reduce skew. The second section is on Tukey's ladder of powers. You will see that log transformations are a special case of the ladder of powers. Finally, we cover the relatively advanced topic of the Box-Cox transformation.
• 16.2: Log Transformations
The log transformation can be used to make highly skewed distributions less skewed. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics.
• 16.3: Tukey Ladder of Powers
Tukey (1977) describes an orderly way of re-expressing variables using a power transformation.
• 16.4: Box-Cox Transformations
The Box-Cox transformation is a particulary useful family of transformations to convert a non-normal behaving data set into an approximately a normal distribution.
• 16.5: Statistical Literacy
• 16.E: Transformations (Exercises)

This page titled 16: Transformations is shared under a Public Domain license and was authored, remixed, and/or curated by David Lane via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.