Part 4. Advanced methods

In this part of the book, we’ll consider advanced methods of statistical analysis to round out your data analysis toolkit. The methods in this part play a key role in the growing field of data mining and predictive analytics.

Chapter 13 expands on the regression methods in chapter 8 to cover parametric approaches to data that are not normally distributed. The chapter starts with a discussion of the generalized linear model and then focuses on cases where you’re trying to predict an outcome variable that is either categorical (logistic regression) or a count (Poisson regression).

Dealing with a large number of variables can be challenging, due to the complexity inherent in multivariate data. Chapter 14 describes two popular methods for exploring and simplifying multivariate data. Principal components analysis can be used to transform a large number of correlated variables into a smaller set of composite variables. Factor analysis consists of a set of techniques for uncovering the latent structure underlying a given set of variables. Chapter 14 provides step-by-step instructions for carrying out each.