An R Companion to Applied Regression

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

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.

Author(s): John Fox, Sanford Weisberg
Edition: 3rd
Publisher: SAGE
Year: 2019

Language: English
Commentary: PDF Convert
Pages: 802
Tags: Statistics, Regression Analysis, R

1. Getting Started with R and RStudio
2. Reading and Manipulating Data
3. Exploring and Transforming Data
4. Fitting Linear Models
5. Standard Errors, Confidence Intervals, Tests
6. Fitting Generalized Linear Models
7. Fitting Mixed-Effects Models
8. Regression Diagnostics
9. Drawing Graphs
10. An Introduction to R Programming