Linear Models with R

This is not a book I recommend to learn R, especially considering that this R programming book is almost a decade old; however, there is a lot of value here. I would expect this book to serve as a graduate-level statistics textbook, as it assumes a high-level knowledge of statistics, linear algebra, and calculus. To summarize the purpose of this book, the goal seems to be to bring reasonably well-informed people to an advanced understanding of regression and ANOVA techniques (linear models). The book does serve that purpose, as the book explains each element with sufficient technical precision to fully understand what is happening without trailing into mathematical statistics more than necessary. In some cases more explanation would be warranted, for example, section 13 “missing data” is only 5 pages which stands out as insufficient coverage of the topic. As a separate note, if you are interested in a thorough coverage of how to handle missing data, I recommend Craig Enders’ book titled “Applied Missing Data Analysis”. That book can be accessed using this commissioned link: https://amzn.to/43GjwoX. Anyway, I believe this book should be considered as either a graduate-level textbook for a linear models class or for a person with a sufficient math background who wants to learn about the topic independently. The book can be found on Amazon using this commissioned link: https://amzn.to/3Y9BbUZ.