Postlude: models and data

Abstract

Becoming a well-rounded data analyst requires more than the skills covered in this book. This postlude sketches some ways in which the types of thinking covered here can be extended to real problems in data analysis. Different ways of evaluating the assumptions of linear regression are considered, including plotting, hypothesis tests, and out-of-sample prediction. If the assumptions are not met, simple linear regression can be extended in various ways, including multiple regression, generalized linear models, and mixed models (among many other possibilities). This postlude concludes with a short discussion of the themes of the book: probabilistic models, methodological pluralism, and the value of elementary statistical thinking.