Introduction To Regression Methods For Public Health Using R Ramzi W Nahhas by Ramzi W. Nahhas 9781032203072, 1032203072 instant download after payment.

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 un-derstanding the associations between an outcome and a set of explana-tory 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. Ex-amples 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. The text includes an overview of regression (Chapter 2); how to exam-ine and summarize the data (Chapter 3), simple (Chapter 4) and multi-ple (Chapter 5) linear regression; binary, ordinal, and conditional lo-gistic regression, and log-binomial regression (Chapter 6); Cox pro-portional hazards regression (survival analysis) (Chapter 7); handling data arising from a complex survey design (Chapter 8); and multiple imputation of missing data (Chapter 9). Each chapter closes with a comprehensive set of exercises. Key Features: • Comprehensive coverage of the most commonly used regression methods, as well as how to use regression with complex survey data or missing data • Accessible to those with only a first course in statistics • Serves as a course textbook, as well as a reference for public health and clinical researchers seeking to learn regression and/or how to use R to do regression analyses • Includes examples of how to diagnose the fit of a regression model • Includes examples of how to summarize, visualize, table, and write up the results • Includes R code to run the examples