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El. knyga: Course in Categorical Data Analysis

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Categorical data-comprising counts of individuals, objects, or entities in different categories-emerge frequently from many areas of study, including medicine, sociology, geology, and education. They provide important statistical information that can lead to real-life conclusions and the discovery of fresh knowledge. Therefore, the ability to manipulate, understand, and interpret categorical data becomes of interest-if not essential-to professionals and students in a broad range of disciplines.

Although t-tests, linear regression, and analysis of variance are useful, valid methods for analysis of measurement data, categorical data requires a different methodology and techniques typically not encountered in introductory statistics courses. Developed from long experience in teaching categorical analysis to a multidisciplinary mix of undergraduate and graduate students, A Course in Categorical Data Analysis presents the easiest, most straightforward ways of extracting real-life conclusions from contingency tables. The author uses a Fisherian approach to categorical data analysis and incorporates numerous examples and real data sets. Although he offers S-PLUS routines through the Internet, readers do not need full knowledge of a statistical software package.

In this unique text, the author chooses methods and an approach that nurtures intuitive thinking. He trains his readers to focus not on finding a model that fits the data, but on using different models that may lead to meaningful conclusions. The book offers some simple, innovative techniques not highighted in other texts that help make the book accessible to a broad, interdisciplinary audience. A Course in Categorical Data Analysis enables readers to quickly use its offering of tools for drawing scientific, medical, or real-life conclusions from categorical data sets.

Recenzijos

"presents an interesting and alternative view of categorical data analysis (CDA)This book would be appropriate for upper undergraduates or master's level courses in statistics for non-majors who need an overview of CDA without going into great detail and theoryrepresents a fresh perspective on CDA. It is well worth a look, both by practitioners who use these methods in their research and by instructors who plan to teach courses on this subject The author has extensive teaching experience at the University of Wisconsin-Madison and at the University of Edinburgh, and the choice of topics in this book reflects that experience Each chapter is followed by useful exercises that should aid in developing an understanding of the presented material Its many biological and medical examples, some developed in detail, make it especially useful for students with interests in the health sciences." --C. B. Borkowf, National Cancer Institute, Bethesda, MD, in Biometrics, December 2000

"This book offers something differentwhat a wealth of detail and insight he develops! Copious numerical examples are discussed alongside the theory, and each of these are interpreted in the context of the study that generated the dataa welcome addition to the literature." --J. M. Juritz, Short Book Reviews of the ISI, April 2000

"a unique work in the implementation of techniques and methodologies neededare not usually found in introductory statistics coursesHighly recommended for upper-division undergraduates and graduate students, faculty, and professionals." --D. J. Gougeon, University of Scranton in CHOICE



"This book would be appropriate for upper undergraduate or master's level courses in statistics for non-majors who need an overview of CDA without going into great detail and theory. Its many biological and medical examples, some developed in detail, make it especially useful for students with interests in the health sciences." C.B. Borkowf, National Cancer Institute, Bethesda, maryland, USA "the excellent discussion of Simpson's paradox, could be included in more general survey courses." C.B. Borkowf, National Cancer Institue, Bethesda, Maryland, USA "this book represents a fresh perspective on CDA. It is well worth a look, both by practitioners who use these methods in their research and by instructors who plan to teach courses on this subject." C.B. Borkowf, National Cancer Institute, Bethesda, Maryland, USA "... this is a very useful little book that serves as an excellent introduction to S-PLUS commands..." -Journal of the Royal Statistical Society This ia a very useful handbookaccessible introduction and quick reference to S-PLUS." -Short Book Reviews of the ISI "written in a clear and lucid stylean excellent candidate for a beginning level graduate textbook on statistical methodsa useful reference for practitioners." -Zentralblatt für Mathematik

Preface
Special Software
Sampling Distributions
1(34)
Experimental design for a population proportion
1(5)
Further properties of the binomial distribution
6(2)
Statistical procedures for the binomial distribution
8(4)
The Poisson distribution
12(3)
Statistical procedures for the Possion distribution
15(2)
The multinomial distribution
17(2)
Sir Ronald Fisher's conditioning result
19(1)
More general sampling models
20(2)
Generalising the binomial distribution
22(3)
The discrete exponential family of distributions
25(4)
Generalising the multinomial distribution
29(6)
Exercises
30(5)
Two-by-Two Contingency Tables
35(30)
Conditional probability and independence
35(1)
Independence of rows and columns
36(1)
Investigating independence, given observational data
37(4)
Edwards' theorem
41(3)
Log-contrasts and the multinomial distribution
44(1)
The log-measure-of-association test
45(3)
The product binomial model
48(3)
The independent Poisson model
51(5)
Fisher's exact test
56(2)
Power properties of our test procedures
58(7)
Exercises
59(6)
Simpson's Paradox and 23 Tables
65(20)
Probability theory
65(2)
The Cornish pixie/Irish leprechaun example
67(2)
Interpretation of Simpson's paradox
69(2)
The three-directional approach
71(4)
Measure of association analysis for 23 tables
75(3)
Medical example
78(2)
Testing equality for two 2 x 2 tables
80(2)
The three-directional approach to the analysis of 23 tables (summary)
82(3)
Exercises
82(3)
The Madison Drug and Alcohol Abuse Study
85(10)
Experimental design
85(3)
Statistical results (phase 3) of study
88(3)
Further validation of results
91(4)
Exercises
93(2)
Goodman's Full-Rank Interaction Analysis
95(24)
Introductory example (no totals fixed)
95(3)
Methodological developments (no totals fixed)
98(4)
Numerical example (a four-corners model)
102(1)
Methodological developments (overall total fixed)
103(2)
Business school example (overall total fixed)
105(1)
Methodological developments (row totals fixed)
106(2)
Advertising example (row totals fixed)
108(2)
Testing for equality of unconditional cell probabilities
110(1)
Analysis of Berkeley admissions data
111(3)
Further data sets
114(5)
Exercises
114(5)
Further Examples and Extensions
119(12)
Hypertension, obesity, and alcohol consumption
119(6)
The Bristol cervical screening data
125(3)
The multiple sclerosis data
128(1)
The Dundee dental health data
129(2)
Exercises
130(1)
Conditional Independence Models for Two-Way Tables
131(8)
Fixed zeroes and missing observations
131(2)
Incomplete tables
133(1)
Perfectly fitting further cells
134(1)
Complete tables
135(1)
Further data sets
136(3)
Exercises
137(2)
Logistic Regression
139(14)
Review of general methodology
139(6)
Analysing your data using Splus
145(2)
Analysis of the mice exposure data
147(1)
Analysis of space shuttle failure data
148(1)
Further data sets
149(4)
Exercises
150(3)
Further Regression Models
153(12)
Regression models for Poisson data
153(2)
The California earthquake data
155(1)
A generalisation of logistic regression
156(4)
Logistic regression for matched case-control studies
160(2)
Further data
162(3)
Exercises
162(3)
Final Topics
165(10)
Continuous random variables
165(1)
Logistic discrimination analysis
166(3)
Testing the slope and quadratic term
169(1)
Extensions
170(2)
Three-way contingency tables
172(3)
Exercises
173(2)
References 175(6)
Index 181


Leonard, Thomas
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