Michael Friendly, David Meyer

Discrete Data Analysis with R (eBook, PDF)

Visualization and Modeling Techniques for Categorical and Count Data

• Format: PDF

Produktbeschreibung

This text 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 methods for exploring data, spotting unusual features, visualizing fitted models, and presenting results. Along with describing the necessary statistical theory, the authors illustrate the practical application of the techniques to a large number of substantive problems. The data sets and R code are available on a supplementary website.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 562
• Erscheinungstermin: 16. Dezember 2015
• Englisch
• ISBN-13: 9781498725859
• Artikelnr.: 55340045

Autorenporträt

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.

Inhaltsangabe

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.