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 test. We will illustrate these using case studies related to understudying if height is hereditary, described in detail in Chapter Chapter 13, using data to build a baseball team on a budget, described in detail in Chapter Chapter 14, determining if a high-fat diet makes mice heavier, described in detail in Chapter Chapter 16, and examining if their is gender bias in research funding in the Netherlands, described in detail in Chapter Chapter 17.