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Regression Models for Categorical and Count Data

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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Regression Models for Categorical Dependent Variables Using Stata

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

Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal;

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Statistical Analysis of Categorical Data

of topics necessary to develop a well-rounded understanding of modern categorical data analysis, including: Logistic regression and log-linear;

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Applied Categorical and Count Data Analysis

Developed from the authors' graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the;

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Cambridge Series in Statistical and Probabilistic Mathematics

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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Regression Models for Categorical, Count, and Related Variables

Social science and behavioral science students and researchers are often confronted with data that are categorical, count a phenomenon, or;

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Advanced Regression Models with SAS and R

for each regression. Discusses data that are categorical, count, proportions, right-skewed, longitudinal and hierarchical;

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Advanced Regression Models with SAS and R

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

Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression;

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Regression Models For Time Series Analysis

models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors;

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Categorical Data Analysis

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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Bayesian Models for Categorical Data

, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to;

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Interpreting and Visualizing Regression Models Using Stata

ordinal logistic regression), and models involving complex survey data. The examples illustrate the use of the margins, marginsplot, contrast, and;

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Regression Models for Categorical and Limited Dependent Variables

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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Biostatistical Methods

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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Categorical Data Analysis Using SAS

Statisticians and researchers will find Categorical Data Analysis Using SAS, Third Edition, by Maura Stokes, Charles Davis, and Gary Koch;

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An Introduction to Categorical Data Analysis

, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these;

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Statistical Regression Modeling with R

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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Statistical Regression Modeling with R

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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Handbook for Applied Modeling

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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Statistical Methods for Categorical Data Analysis

This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social;

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Regression With Social Data

response models* Poisson, negative binomial, and other regression models for event-count data* Survival analysis using multistate, multiepisode;

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Handbook for Applied Modeling

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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Regression Analysis of Count Data

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: Models, Methods and Applications

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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Foundations of Linear and Generalized Linear Models

, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count;

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