By: Jeffrey S Simonoff
496 pages, 64 illus
Introduction with broad coverage, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and tables with ordered categories; and regression models for two-category (binary) and multiple-category target variables, such as logistic and proportional odds models.
Introduction.- Gaussian-Based Data Analysis.- Gaussian-Based Model Building.- Categorical Data and Goodness-of-Fit.- Regression Models for Count Data.- Analyzing Two-Way Tables.- Tables with More Structure.- Multidimensional Contingency Tables.- Regression Models for Binary Data.- Regression Models for Multiple Category Response Data.
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