Welcome

Thank you for purchasing the MEAP edition of Regression: A friendly guide! Regression models are great options for analyzing relationships in data and for predicting outcomes, especially when interpretability is important -- explaining how to interpret and apply these models is therefore a basic goal of the book. After working through it, you’ll know what all of the lingo, notation, and output mean, you’ll know how to cut through the superficial hype surrounding regression and other data science tools, and you’ll know how to make sound, defensible data-driven decisions. Instead of just diving head-first into the theory, I’ll help you build up your intuition and knowledge incrementally, using carefully chosen datasets to motivate and illuminate things.

This book will teach you how to build, assess, and interpret regression models, from simple models with one predictor in Part 1 to complex models with many variables in Parts 3 and 4. You’ll learn to work with linear regression, logistic regression, regression for counts, robust regression and penalization, and generalized additive models. Along the way, you’ll also learn about simulation methods and modern tools like the bootstrap and cross-validation in Part 2.