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Modeling Binary Correlated Responses using SAS, SPSS and R

  • Jeffrey R. Wilson
  • Kent A. Lorenz

Part of the ICSA Book Series in Statistics book series (ICSABSS, volume 9)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Introduction and Review of Modeling Uncorrelated Observations

    1. Front Matter
      Pages 1-1
    2. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 3-16
    3. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 17-23
    4. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 25-54
  3. Analyzing Correlated Data Through Random Component

    1. Front Matter
      Pages 55-55
    2. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 57-79
    3. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 81-102
    4. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 103-130
    5. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 131-146
    6. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 147-165
  4. Analyzing Correlated Data Through Systematic Components

    1. Front Matter
      Pages 167-167
    2. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 169-200
    3. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 201-224
    4. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 225-246
  5. Analyzing Correlated Data Through the Joint Modeling of Mean and Variance

    1. Front Matter
      Pages 247-247
    2. Jeffrey R. Wilson, Kent A. Lorenz
      Pages 249-264

About this book

Introduction

Statistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, and SPSS allows for easy implementation by readers. For readers interested in learning more about the languages, though, there are short tutorials in the appendix. Accompanying data sets are available for download through the book s website. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects.  Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.

Keywords

analysis of correlated binary data biomedical data correlated data data analysis with software statistical methods for health research statistical models for health data

Authors and affiliations

  • Jeffrey R. Wilson
    • 1
  • Kent A. Lorenz
    • 2
  1. 1.Arizona State UniversityDepartment of Economics, W.P. Carey SchoTempeUSA
  2. 2.School of Nutrition & Health PromotionArizona State University College of Health SolutionsPhoenixUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-23805-0
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-23804-3
  • Online ISBN 978-3-319-23805-0
  • Series Print ISSN 2199-0980
  • Series Online ISSN 2199-0999
  • Buy this book on publisher's site
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