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An R Companion to Applied Regression
About this book
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.
"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."
–Christopher Hare, University of California, Davis
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Information
Table of contents
- Cover
- Half Title
- Acknowledgements
- Title Page
- Copyright Page
- Contents
- Publisher Note
- Preface
- About the Authors
- 1 Getting Started With R and RStudio
- 2 Reading and Manipulating Data
- 3 Exploring and Transforming Data
- 4 Fitting Linear Models
- 5 Coefficient Standard Errors, Confidence Intervals, and Hypothesis Tests
- 6 Fitting Generalized Linear Models
- 7 Fitting Mixed-Effects Models
- 8 Regression Diagnostics for Linear, Generalized Linear, and Mixed-Effects Models
- 9 Drawing Graphs
- 10 An Introduction to R Programming
- References
- Subject Index
- Data Set Index
- Data Set Index
- Data Set Index