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Modern Regression Techniques Using R

Modern Regression Techniques Using R
A Practical Guide

February 2009 | 216 pages | SAGE Publications Ltd
Statistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books' webpage:

Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter.

Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets.

The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.

Very Brief Introduction to R
Very brief introduction to R
The basic regression
ANOVA as regression
ANCOVA: Lord's paradox and mediation analysis
Model selection and shrinkage
Generalized linear models (GLMs)
Regression splines and generalized additive models (GAMs)
Multilevel models
Robust regression
Conclusion - make your data cool

`An impressive resource for lecturers and researchers in a relatively slim text. I particularly like the way it rapidly builds on basic regression models to introduce genuinely advanced and cutting edge techniques. It is also very useful that the examples are implemented in the free, cross-platform statistical software environment R' - Dr Thom Baguley, Psychology, Nottingham Trent University

Nice compact format. Recommended only for advanced courses in econometrics as some topics are presented but not explained (e.g. diagnostic plots in chapter "Basic regression".

Dr Sönke Hoffmann
Faculty of Economics, University of Magdeburg
September 23, 2013

The book is an invaluable resource for those students wanting to learn more about R and advanced statistics techniques.
In our University, at the Department of Sociology we have recently started to shift our statistics classes from SPSS-centric to R-philosophy. In this respect, R-based books that treat commonly used techniques like regression are most welcomed resources, as it is not only the students' training that we need to change, but most frequently our colleagues' misperceptions due to practice over the years.

Dr Adrian Dusa
Department of Sociology and Social Work, University of Bucharest
March 11, 2010

It seems like a very good text for an introductory course, and I am going to recommend that students use it as a supplemental test and see how well they do.

Dr Kenneth Benoit
Dept of Political Science, University of Dublin Trinity College
November 12, 2009

I pushed for using R in the course, but was not allowed to make change (we are sticking with Excel as data analysis tool.)

Good book with great examples!

Mr Brian Kreeger
Business Administration, Metropolitan State University
October 23, 2009

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ISBN: 9781847879035
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