β Scribed by Friendly, Michael; Meyer, David
No coin nor oath required. For personal study only.
An Applied Treatment of Modern Graphical Methods for Analyzing Categorical Data Discrete 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 methods for exploring data, spotting unusual features, visualizing Read more...
Front Cover
Contents
Preface
Part I: Getting Started
1: Introduction
2: Working with Categorical Data
3: Fitting and Graphing Discrete Distributions
Part II: Exploratory and Hypothesis-Testing Methods
4: Two-Way Contingency Tables
5: Mosaic Displays for n-Way Tables
6: Correspondence Analysis
Part III: Model-Building Methods
7: Logistic Regression Models
8: Models for Polytomous Responses
9: Loglinear and Logit Models for Contingency Tables
10: Extending Loglinear Models
11: Generalized Linear Models for Count Data
References
Colophon
Back Cover.
Mathematics -- Data processing;R (Computer program language)
π SIMILAR VOLUMES
A early publishing of CRC Press. No the final one.
<p><span>This text provides practical guidance on conducting regression analysis on categorical and count data. Step by step and supported by lots of helpful graphs, it covers both the theoretical underpinnings of these methods as well as their application, giving you the skills needed to apply them
<p><span>Developed from the authorsβ graduate-level biostatistics course, </span><span>Applied Categorical and Count Data Analysis, Second Edition </span><span>explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors have been teaching