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Interaction Effects in Linear and Generalized Linear Models: Examples and Applications Using Stata

Offering a clear set of workable examples with data and explanations, Interaction Effects in Linear and Generalized Linear Models is a;

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Generalized Linear Mixed Models

Generalized Linear Mixed Models: Modern Concepts, Methods and Applications presents an introduction to linear modeling using the;

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Multilevel and Longitudinal Modeling Using Stata, Volumes I and II, Third Edition

chapters. The chapters are organized in four parts. Volume II discusses generalized linear mixed models for binary, categorical, count, and;

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Generalized Linear and Nonlinear Models for Correlated Data

Edward F. Vonesh's "Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS" is devoted to the;

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An Introduction to Modern Econometrics Using Stata

Econometrics Using Stata focuses on the role of method-of-moments estimators, hypothesis testing, and specification analysis and provides practical;

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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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Linear and Generalized Linear Mixed Models and Their Applications

This book covers two major classes of mixed effects models, linear mixed models and generalized linear mixed models. It presents an up-to;

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Multivariate Generalized Linear Mixed Models Using R

Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data;

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Foundations of Linear and Generalized Linear Models

of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book;

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Data Analysis Using Hierarchical Generalized Linear Models with R

within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to;

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Data Analysis Using Hierarchical Generalized Linear Models with R

within the field and, using data examples, illustrates how to analyse various kinds of data using R. It provides a likelihood approach to;

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An Introduction to Generalized Linear Models

Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive;

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Introduction to General and Generalized Linear Models

necessary likelihood theory, the book covers both general and generalized linear models using the same likelihood-based methods. It presents the;

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A First Course in Linear Model Theory

. Revised or expanded topics include linear fixed, random and mixed effects models, generalized linear models, Bayesian and hierarchical linear;

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Generalized Linear Models with Random Effects

This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and;

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Generalized Linear and Nonlinear Models for Correlated Data

Edward F. Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the;

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Generalized Linear Models with Random Effects: Unified Analysis Via H-Likelihood, Second Edition

This is the second edition of a monograph on generalized linear models with random effects that extends the classic work of McCullagh and;

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Generalized Linear Models with Random Effects

with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and;

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Handbook Of Statistical Analyses Using Stata

, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and;

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Handbook of Statistical Analyses Using Stata, Fourth Edition

, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and;

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Handbook of Statistical Analyses Using Stata

, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and;

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A Handbook of Statistical Analyses Using Stata

, variance analysis, multiple linear regression, logistic regression, generalized linear models, survival analysis, random effects models, and;

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Regression Methods in Biostatistics

interaction of causal effects in essentially the same way. The examples, analyzed using Stata, are drawn from the biomedical context but generalize to;

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An Introduction to Generalized Additive Models with R

splines, and builds a well-grounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the;

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Generalized Linear Models and Extensions

Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory;

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Multilevel and Longitudinal Modeling Using Stata, Volume I

Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. The first;

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Advanced R Statistical Programming and Data Models

linear models, generalized additive models, mixed effects models, machine learning, and parallel processing Carry out regression modeling using R;

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