Introduction to Regression Methods for Public Health using R
2021-10-22
Welcome
Thanks for visiting!
This text is UNDER CONSTRUCTION. If you have any comments or suggestions, feel free to contact me at ramzi.nahhas@wright.edu. Thank you!
Additionally, if you have a dataset that you think would be suitable for inclusion in this text (as an example or for an exercise), I would love to hear about it.
Use
Regression Methods for Public Health using R by Ramzi W. Nahhas is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Preface
This text is suitable as a second biostatistics course for Master of Public Health students or public health professionals. Almost all public health students take an introductory biostatistics course, providing a foundation but perhaps not enough to use more advanced methods. There are a plethora of textbooks covering advanced topics such as multiple regression, logistic regression, survival analysis, and others. These texts, however, are generally aimed at those with a background in mathematical statistics and/or without a focus on public health. The goal of this text is to bridge that gap - to provide a gentle introduction to regression methods that covers all the basics and a bit more, and with examples drawn from public health data. There are excellent texts that cover each of the regression methods covered herein, as well as R programming, in much greater detail than is provided here. My hope is that for those who feel those texts are beyond their reach, what you learn here will give you the knowledge, skills, and confidence to go further in using regression and R.
Software information and conventions
The knitr package (Xie 2015) and the bookdown package (Xie 2021) were used to compile this text. The R session information is shown below:
xfun::session_info()## R version 4.0.5 (2021-03-31)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
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## Package version:
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## yaml_2.2.1Package names and inline code are formatted in a typewriter font (e.g., knitr, lm(Y ~ X)), and function names are followed by parentheses (e.g., lm()).
Acknowledgments
Names