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regression analysis on categorical and count data. The author covers both the theory and application of statistical models, with the help;
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Models for Categorical Dependent Variables Using Stata, Second Edition , fills this void, showing how to fit and interpret regression models for;
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Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal;
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of topics necessary to develop a well-rounded understanding of modern categorical data analysis, including: Logistic regression and log-linear;
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Developed from the authors' graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the;
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models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book;
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Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or;
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for each regression. Discusses data that are categorical, count, proportions, right-skewed, longitudinal and hierarchical;
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framework for each regression. Discusses data that are categorical, count, proportions, right-skewed, longitudinal and hierarchical;
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Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression;
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models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors;
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Praise for the Second Edition A must-have book for anyone expecting to do research and/or applications in categorical data analysis;
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, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to;
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ordinal logistic regression), and models involving complex survey data. The examples illustrate the use of the margins, marginsplot, contrast, and;
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presents models for count outcomes by beginning with the Poisson regression model and showing how this model leads to the negative binomial model;
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and clinical trials with categorical, count, and event-time data. Presenting a broad scope of coverage and the latest research on the topic;
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Statisticians and researchers will find Categorical Data Analysis Using SAS, Third Edition, by Maura Stokes, Charles Davis, and Gary Koch;
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, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these;
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regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume;
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regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume;
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Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data;
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This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social;
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response models* Poisson, negative binomial, and other regression models for event-count data* Survival analysis using multistate, multiepisode;
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Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data;
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Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a;
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regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal;
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, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count;
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