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Synopsis
Discusses hypothesis testing strategies for the assessment of association in contingency tables and sets of contingency tables. Also discusses various modeling strategies available for describing the nature of the association between a categorical outcome measure and a set of explanatory variables.
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About the Author
Maura E. Stokes is Senior Manager of Statistical Applications Research and Development at SAS Institute. She received her DrPH in Biostatistics from the University of North Carolina at Chapel Hill and has taught and written about categorical data analysis for over fifteen years.
Charles S. Davis is Professor of Biostatistics at the University of Iowa. He received his PhD in Biostatistics from the University of Michigan. His research and teaching interests include categorical data analysis and methods for the analysis of repeated measures.
Gary G. Koch is Professor of Biostatistics and Director of the Biometrics Consulting Laboratory at the University of North Carolina at Chapel Hill. He has had a prominent role in the field of categorical data analysis for the last thirty years. He teaches classes and seminars in categorical data analysis, consults in areas of statistical practice, conducts research, and trains many Biostatistics students.
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