Maura E. Stokes & Charles S. Davis 
Categorical Data Analysis Using SAS, Third Edition [EPUB ebook] 

Wsparcie
Statisticians and researchers will find Categorical Data Analysis Using SAS, Third Edition, by Maura Stokes, Charles Davis, and Gary Koch, to be a useful discussion of categorical data analysis techniques as well as an invaluable aid in applying these methods with SAS. Practical examples from a broad range of applications illustrate the use of the FREQ, LOGISTIC, GENMOD, NPAR1WAY, and CATMOD procedures in a variety of analyses. Topics discussed include assessing association in contingency tables and sets of tables, logistic regression and conditional logistic regression, weighted least squares modeling, repeated measurements analyses, loglinear models, generalized estimating equations, and bioassay analysis.

The third edition updates the use of SAS/STAT software to SAS/STAT 12.1 and incorporates ODS Graphics. Many additional SAS statements and options are employed, and graphs such as effect plots, odds ratio plots, regression diagnostic plots, and agreement plots are discussed. The material has also been revised and reorganized to reflect the evolution of categorical data analysis strategies. Additional techniques include such topics as exact Poisson regression, partial proportional odds models, Newcombe confidence intervals, incidence density ratios, and so on.

This book is part of the SAS Press program.
€92.99
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O autorze

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 forty-five 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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Język Angielski ● Format EPUB ● Strony 590 ● ISBN 9781612900902 ● Rozmiar pliku 25.0 MB ● Wydawca SAS Institute ● Opublikowany 2012 ● Ydanie 3 ● Do pobrania 24 miesięcy ● Waluta EUR ● ID 7026605 ● Ochrona przed kopiowaniem Adobe DRM
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