Introduction to Regression Methods for Public Health Using R - Nahhas, Ramzi W.; - Prospero Internet Bookshop
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Introduction to Regression Methods for Public Health Using R
 
Product details:
ISBN13:9781032203072
ISBN10:1032203072
Binding:Hardback
No. of pages:456 pages
Size:254x178 mm
Weight:453 g
Language:English
Illustrations: 68 Illustrations, black & white; 45 Illustrations, color; 68 Line drawings, black & white; 45 Line drawings, color; 27 Tables, black & white
700
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Introduction to Regression Methods for Public Health Using R

 
Edition number: 1
Publisher: Chapman and Hall
Date of Publication:
 
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Short description:

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.

Long description:

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.


The text includes an overview of regression (Chapter 2); how to examine and summarize the data (Chapter 3), simple (Chapter 4) and multiple (Chapter 5) linear regression; binary, ordinal, and conditional logistic regression, and log-binomial regression (Chapter 6); Cox proportional 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
Table of Contents:

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