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An R Companion to Applied Regression
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An R Companion to Applied Regression

3 Revised edition

Book Details

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 17.90 x 25.30 x 3.00 cms
Ksh 20,500.00
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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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