Linear Models

Up to this point, this book has focused mainly on datasets consisting of a single variable. However, in data analyses challenges, it is very common to be interested in the relationship between two or more variables. In this part of the book we introduce linear models, a general framework that unifies approaches used for analyzing association between variables, such as simple and multivariate regression, treatment effect models, and association tests. We will illustrate these using case studies related to understudying if height is hereditary, described in detail in Chapter 14  Regression, using data to build a baseball team on a budget, described in detail in Chapter 15  Multivariate Regression, determining if a high-fat diet makes mice heavier, described in detail in Chapter 17  Treatment effect models, and examining if their is gender bias in research funding in the Netherlands, described in detail in Chapter 18  Association tests.