Daniel Zelterman applies his extensive SAS knowledge and biostatistics experience to illustrate how to use the GENMOD procedure to analyze log-linear models for categorical data. His wide variety of examples illustrate the statistical applications PROC GENMOD can perform. He thoroughly describes the models, provides real data examples, supplies the necessary code, and explains the output from GENMOD. The topics covered include: the Pearson goodness of fit statistic; tables of categorical data; a review of log-linear model methods for rectangular tables of categorical data; extrapolation methods to estimate population size; new models and distributions for statistical analysis of data; and issues in power analysis and estimating sample size in experiments. The models take advantage of the wide class of generalized linear models and use real data from pharmaceutical studies and epidemiology, wildlife, and government statistics. Statisticians who have a basic understanding both of SAS and the analysis of categorical data will greatly benefit from this book. The discussion of each model and method emphasizes statistical aspects, such as interpretation of results, rather than programming skills. The numerous examples are used to motivate the theory and methods as they are discussed.
By This Author
- Curt Hinrichs has been with the JMP Division of SAS since 2006 and is Senior Manager of JMP Academic Programs.
- Chuck Boiler is the U.S. Systems Engineer Manager for JMP, a business unit of SAS.
- A SAS user since 1986, Robert Obenchain is Principal Consultant at Risk Benefit Statistics LLC in Carmel, Indiana.
- Douglas E. Faries is Senior Research Advisor at Lilly USA, where he oversees statistical design and analysis support for Health Outcomes Research.