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An R Companion to Applied Regression - 3 Revised edition

By: (Author) John Fox , (Author) Sanford Weisberg

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Ksh 20,500.00

Format: Paperback / Softback

ISBN-10: 1544336470

ISBN-13: 9781544336473

Edition: 3 Revised edition

Publisher: SAGE Publications Inc

Imprint: SAGE Publications Inc

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Oct 16th, 2018

Print length: 608 Pages

Weight: 1,124 grams

Dimensions (height x width x thickness): 17.90 x 25.30 x 3.00 cms

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An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis.
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. 

"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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