![]() And if you’ve been following along with Data Journalism wtih R, you know that means the code in the book is easier to read and there are some solid underlying principles. This is the second edition of the text, and most of the changes are converting the previous edition to tidyverse principles. This tutorial is based in part on the excellent book that came out last year, “Analyzing Baseball Data with R” by Max Marchi, Jim Albert, and Ben Baumer. There are a ton of books, blog posts, and lectures covering these topics in greater depth (and we’ll link to those in the notes at the bottom), but we wanted to distill some of this information into a single post you can bookmark and revisit whenever you’re considering running a linear regression. In this post we’ll cover the assumptions of a linear regression model.
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