An updated version to meet the requirements of the new A Level specifications being offered by all the awarding bodies in 2000. Chapters are the same as before but there is extra material within them. Chapter 1 now includes several examples of the graphical comparision of similar data sets.This chapter includes five new sections and ends with a discussion of the (largely unwanted) characteristics to be expected in real data. Chapter 2 has been augmented by sections on the use of coded values, Bayes' theorem is included in Chapter 4, and the method for determining the distribution of asimple function of a random variable is now included in Chapter 9. In Chapter 14 there is a new section dealing with properties of regression line estimators and, later, a subsection on nonsense correlation. Some questions have been introduced on sampling in Chapter 3. These questions are somewhatopen-ended, as were a number of existing questions that were not given answers in the first edition. In this edition possible answers are now provided to these questions.

Introducing Statistics

Science & Math

Cook, Ian

Upton, Graham J.G.

Mathematics

Oxford University Press

This is the second of two volumes dealing with aspects of the analysis of spatial data. The first volume concentrated on describing recent advances made in methods for the analysis of point pattern data and on a synopsis of the regression methods available for the analysis of quantitative spatial data.

Categorical and Directional Data, Volume 2, Spatial Data Analysis by Example

Politics & Social Sciences

Fingleton, Bernard

Social Sciences

wiley

The Analysis of Cross-tabulated Data

Introduces the key concepts in the analysis of categoricaldata with illustrative examples and accompanying R code This book is aimed at all those who wish to discover how to analyze categorical data without getting immersed in complicated mathematics and without needing to wade through a large amount of prose. It is aimed at researchers with their own data ready to be analyzed and at students who would like an approachable alternative view of the subject. Each new topic in categorical data analysis is illustrated with an example that readers can apply to their own sets of data. In many cases, R code is given and excerpts from the resulting output are presented. In the context of log-linear models for cross-tabulations, two specialties of the house have been included: the use of cobweb diagrams to get visual information concerning significant interactions, and a procedure for detecting outlier category combinations. The R code used for these is available and may be freely adapted. In addition, this book: <ul>
<li>Uses an example to illustrate each new topic in categorical data</li> <li>Provides a clear explanation of an important subject</li> <li>Is understandable to most readers with minimal statistical and mathematical backgrounds</li> <li>Contains examples that are accompanied by R code and resulting output</li> <li>Includes starred sections that provide more background details for interested readers</li> </ul>
Categorical Data Analysis by Example is a reference for students in statistics and researchers in other disciplines, especially the social sciences, who use categorical data. This book is also a reference for practitioners in market research, medicine, and other fields.

Categorical Data Analysis by Example

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