Modeling Binary Correlated Responses using SAS, SPSS and R

Nonfiction, Science & Nature, Mathematics, Statistics, Science, Biological Sciences
Cover of the book Modeling Binary Correlated Responses using SAS, SPSS and R by Jeffrey R. Wilson, Kent A. Lorenz, Springer International Publishing
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Author: Jeffrey R. Wilson, Kent A. Lorenz ISBN: 9783319238050
Publisher: Springer International Publishing Publication: October 12, 2015
Imprint: Springer Language: English
Author: Jeffrey R. Wilson, Kent A. Lorenz
ISBN: 9783319238050
Publisher: Springer International Publishing
Publication: October 12, 2015
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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