Regression Models For Data Science In R

This book is designed as a companion to the Regression Models Coursera class as part of the Data
Science Specialization, a ten-course program offered by three faculty, Jeff Leek, Roger Peng and
Brian Caffo, at the Johns Hopkins University Department of Biostatistics. The videos associated with this book can be watched in full here, though the relevant links to specific videos are placed at the appropriate locations throughout. Before beginning, we assume that you have a working knowledge of the R programming language.

If not, there is a wonderful Coursera class by Roger Peng, that can be found here. In addition, students should know the basics of frequentist statistical inference. There is a Coursera class here and a LeanPub book here. The entirety of the book is on GitHub here. Please submit pull requests if you find errata! In addition, the course notes can also be found on GitHub here. While most code is in the book, all of the code for every figure and analysis in the book is in the R markdown files (.Rmd) for the respective lectures.

Finally, we should mention swirl (statistics with interactive R programming). swirl is an intelligent
tutoring system developed by Nick Carchedi, with contributions by Sean Kross and Bill and Gina
Croft. It offers a way to learn R in R. Download swirl here. There’s a swirl module for this course!.
Try it out, it’s probably the most effective way to learn.