# 16: Transformations

- Page ID
- 2186

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.

## Contributors and Attributions

Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University.