Regression for Health and Social Science
Applied Linear Models with R

Real-life examples and exercises emphasize interpretation of statistical linear models and computer output using a minimum of mathematics.

Daniel Zelterman (Author)

9781108478182, Cambridge University Press

Hardback, published 12 May 2022

294 pages
25 x 17.4 x 1.8 cm, 0.68 kg

This textbook for students in the health and social sciences covers the basics of linear model methods with a minimum of mathematics, assuming only a pre-calculus background. Numerous examples drawn from the news and current events with an emphasis on health issues, illustrate the concepts in an immediately accessible way. Methods covered include linear regression models, Poisson regression, logistic regression, proportional hazards regression, survival analysis, and nonparametric regression. The author emphasizes interpretation of computer output in terms of the motivating example. All of the R code is provided and carefully explained, allowing readers to quickly apply the methods to their own data. Plenty of exercises help students think about the issues involved in the analysis and its interpretation. Code and datasets are available for download from the book's website at www.cambridge.org/zelterman

Preface
Preface to revised edition
Acknowledgments
1. Introduction
2. Principles of statistics
3. Introduction to linear regression
4. Assessing the regression
5. Multiple linear regression
6. Indicators, interactions, and transformations
7. Nonparametric statistics
8. Logistic regression
9. Diagnostics for logistic regression
10. Poisson regression
11. Survival analysis
12. Proportional hazards regression
13. Review of methods
Appendix: statistical distributions
Selected solutions and hints
References
Index.

Subject Areas: Probability & statistics [PBT], Epidemiology & medical statistics [MBNS], Social research & statistics [JHBC]