# An R Companion to Applied Regression

- John Fox - McMaster University, Canada
- Sanford Weisberg - University of Minnesota, USA

**Other Titles in:**

Regression & Correlation | Research Methods in Psychology | Sociological Research Methods

**An R Companion to Applied Regression**is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials.

The **Third Edition **has been reorganized and includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the car and effects packages for R for this edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text.

### Supplements

An **accompanying website** for the book found at study.sagepub.com/RCompanion provides:

- R scripts for examples by chapter
- Data files used in the book
- The car package (Companion to Applied Regression), an accompanying software for regression diagnostics and other regression-related tasks
- Other resources to help students get the most out of the text

“**An R Companion to Applied Regression **continues to provide the most comprehensive and user-friendly guide to estimating, interpreting, and presenting results from regression models in R.”

**University of California, Davis**

“This is the best book I’ve read for teaching the modern practice of regression. By going deeply into both R and applied regression, it manages to use each topic to motivate and illustrate the other. The whole is much greater than sum of the parts because each thread so effectively reinforces the other. There are many nice surprises in this new edition. R Studio and markdown are used to encourage a reproducible workflow. There’s an excellent and accessible chapter on mixed and longitudinal data that expands the reach of regression methods to the much more complex data structures typical of current practice. Like its predecessors, this edition is a model of clear, thoughtful exposition. It’s an outstanding contribution to the teaching and practice of regression.”

**York University**

“This is an impressive update to a book I have long admired. The authors have brought the description of how to do data analysis and plots of Applied Regression related data to a modern and more comprehensive level.”

**York University**

Made a good supplement with a heavy emphasis on R.

**Mathematics Dept, University Of North Dakota**