Introduction to Regression Methods for Public Health Using R [Hardcover]

Preface? 1. Introduction? 2. Overview of Regression Methods? 3. Data Summarization? 4. Simple Linear Regression? 5. Multiple Linear Regression? 6. Binary Logistic Regression? 7. Survival Analysis? 8. Analyzing Complex Survey Data? 9. Multiple Imputation of Missing Data? Appendix A. Datasets? Bibliography? Index

This book?teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields.

Ramzi W. Nahhas teaches biostatistics at Wright State University, Dayton, Ohio, USA, where he is Professor in the Department of Population and Public Health Sciences, Boonshoft School of Medicine. In addition to teaching, he is actively involved in research collaborations with faculty, residents, and students, primarily in his own department and the Department of Psychiatry.

Introduction to Regression Methods for Public Health Using R teaches regression methods for continuous, binary, ordinal, and time-to-event outcomes using R as a tool. Regression is a useful tool for understanding the associations between an outcome and a set of explanatory variables, and regression methods are commonly used in many fields, including epidemiology, public health, and clinical research. The focus of this book is on understanding and fitting regression models, diagnosing model fit, and interpreting and writing up results. Examples are drawn from public health and clinical studies. Designed for students, researchers, and practitioners with a basic understanding of introductory statistics, this book teaches the basics of regression and how to implement regression methods using R, allowing the reader to enhance their understanding and begin to grasp new concepts and models.

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