Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data

by Michael Friendly, David Meyer

Overview

An Applied Treatment of Modern Graphical Methods for Analyzing Categorical DataDiscrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data. It explains how to use graphical meth

Product Details

ISBN-13:	9781498725866
Publisher:	CRC Press
Publication date:	12/16/2015
Series:	Chapman & Hall/CRC Texts in Statistical Science , #120
Sold by:	Barnes & Noble
Format:	eBook
Pages:	562
File size:	26 MB
Note:	This product may take a few minutes to download.

About the Author

Michael Friendly is a professor of psychology, founding chair of the Graduate Program in Quantitative Methods, and an associate coordinator with the Statistical Consulting Service at York University. He earned a PhD in psychology from Princeton University, specializing in psychometrics and cognitive psychology. In addition to his research interests in psychology, Professor Friendly has broad experience in data analysis, statistics, and computer applications. His main research areas are the development of graphical methods for categorical and multivariate data and the history of data visualization. He is an associate editor of the Journal of Computational and Graphical Statistics and Statistical Science.

David Meyer is a professor of business informatics at the University of Applied Sciences Technikum Wien. He earned a PhD in business administration from the Vienna University of Economics and Business, with an emphasis on computational economics. Dr. Meyer has published numerous papers in various computer science and statistical journals. His research interests include R, business intelligence, data mining, and operations research.

Table of Contents

Getting Started: Introduction. Working with Categorical Data. Fitting and Graphing Discrete Distributions. Exploratory and Hypothesis-Testing Methods: Two-Way Contingency Tables. Mosaic Displays for n-Way Tables. Correspondence Analysis. Model-Building Methods: Logistic Regression Models. Models for Polytomous Responses. Loglinear and Logit Models for Contingency Tables. Extending Loglinear Models. Generalized Linear Models for Count Data.

Editorial Reviews

"This is an excellent book, nearly encyclopedic in its coverage. I personally find it very useful and expect that many other readers will as well. The book can certainly serve as a reference. It could also serve as a supplementary text in a course on categorical data analysis that uses R for computation or—because so much statistical detail is provided—even as the main text for a course on the topic that emphasizes graphical methods."
—John Fox, McMaster University

"For many years, Prof. Friendly has been the most effective promoter in Statistics of graphical methods for categorical data. We owe thanks to Friendly and Meyer for promoting graphical methods and showing how easy it is to implement them in R. This impressive book is a very worthy addition to the library of anyone who spends much time analyzing categorical data." (Alan Agresti, Biometrics)

From the Publisher