Solutions Manual Logistic Regression Models

Solutions Manual Logistic Regression Models
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ISBN-10 : 143982066X
ISBN-13 : 9781439820667
Rating : 4/5 (6X Downloads)

Book Synopsis Solutions Manual Logistic Regression Models by : Taylor & Francis Group

Download or read book Solutions Manual Logistic Regression Models written by Taylor & Francis Group and published by . This book was released on 2009-07-16 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides Comprehensive Coverage of All Types of Logistic Models Based on a successful course taught by the authorLogistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, and categorical response regression procedures. It illustrates how to apply the models to medical, health, environmental/ecological, physical, and social science data. Due to its broad scope of capabilities, Stata is used to develop, evaluate, and display most models. R code is also supplied for replicating many of the examples. Many examples help explain the concepts and techniques of successful logistic modelingThe text first provides basic terminology and concepts, before explaining the foremost methods of estimation (maximum likelihood and iteratively reweighted least squares) appropriate for logistic models. It then presents an in-depth discussion on related terminology and examines logistic regression model development and interpretation of the results. After focusing on various interactions, the author evaluates assumptions and goodness-of-fit tests that can be used for model assessment. He also covers binomial logistic regression, varieties of overdispersion, and a number of extensions to the basic binary and binomial logistic model. Apply the models to your own dataFrom binary to multinomial, panel, survey, and exact models, this text covers all types of logistic regression models. Supplying code, commonly used commands, and other useful information in the appendices, it shows how to use the models to understand data from a variety of disciplines.


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