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Modeling Binary Correlated Responses using SAS, SPSS and R

2015-10-12 Mathematics
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Release : 2015-10-12
Genre : Mathematics
Kind : eBook
Book Rating : 051/5 ( reviews)

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Book Synopsis Modeling Binary Correlated Responses using SAS, SPSS and R by : Jeffrey R. Wilson

Download or read book Modeling Binary Correlated Responses using SAS, SPSS and R written by Jeffrey R. Wilson. This book was released on 2015-10-12. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Modeling Binary Correlated Responses

2024-09-09 Mathematics
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Release : 2024-09-09
Genre : Mathematics
Kind : eBook
Book Rating : 261/5 ( reviews)

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Book Synopsis Modeling Binary Correlated Responses by : Jeffrey R. Wilson

Download or read book Modeling Binary Correlated Responses written by Jeffrey R. Wilson. This book was released on 2024-09-09. Available in PDF, EPUB and Kindle. Book excerpt: This book is an updated edition of Modeling Binary Correlated Responses Using SAS, SPSS and R, and now it includes the use of STATA. It uses these Statistical tools to analyze correlated binary data, accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages, as well as showcase both traditional and new methods for application to health-related research. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Short tutorials are in the appendix, for readers interested in learning more about the languages. 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, SPSS and STATA, allows for easy implementation by readers. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.

Modeling Binary Correlated Responses

Download Modeling Binary Correlated Responses PDF Online Free

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Release :
Genre :
Kind : eBook
Book Rating : 270/5 ( reviews)

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Book Synopsis Modeling Binary Correlated Responses by : Jeffrey R. Wilson

Download or read book Modeling Binary Correlated Responses written by Jeffrey R. Wilson. This book was released on . Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Regression Modeling with R

2021-04-08 Mathematics
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Release : 2021-04-08
Genre : Mathematics
Kind : eBook
Book Rating : 831/5 ( reviews)

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Book Synopsis Statistical Regression Modeling with R by : Ding-Geng (Din) Chen

Download or read book Statistical Regression Modeling with R written by Ding-Geng (Din) Chen. This book was released on 2021-04-08. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.

Monte-Carlo Simulation-Based Statistical Modeling

2017-02-01 Medical
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Release : 2017-02-01
Genre : Medical
Kind : eBook
Book Rating : 072/5 ( reviews)

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Book Synopsis Monte-Carlo Simulation-Based Statistical Modeling by : Ding-Geng (Din) Chen

Download or read book Monte-Carlo Simulation-Based Statistical Modeling written by Ding-Geng (Din) Chen. This book was released on 2017-02-01. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.

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