Part 3. Intermediate methods

While part 2 covered basic graphical and statistical methods, section 3 offers coverage of intermediate methods. We move from describing the relationship between two variables, to modeling the relationship between a numerical outcome variable and a set of numeric and/or categorical predictor variables.

Chapter 8 introduces regression methods for modeling the relationship between a numeric outcome variable and a set of one or more predictor variables. Modeling data is typically a complex, multistep, interactive process. Chapter 8 provides step-by-step coverage of the methods available for fitting linear models, evaluating their appropriateness, and interpreting their meaning.

Chapter 9 considers the analysis of basic experimental and quasi-experimental designs through the analysis of variance and its variants. Here we’re interested in how treatment combinations or conditions affect a numerical outcome variable. The chapter introduces the functions in R that are used to perform an analysis of variance, analysis of covariance, repeated measures analysis of variance, multi-factor analysis of variance, and multivariate analysis of variance. Methods for assessing the appropriateness of these analyses, and visualizing the results are also discussed.