This textbook for a second course in basic statistics for undergraduates or first-year graduate students introduces linear regression models and describes other linear models including Poisson regression, logistic regression, proportional hazards regression, and nonparametric regression. Numerous examples drawn from the news and current events with an emphasis on health issues illustrate these concepts. Assuming only a pre-calculus background, the author keeps equations to a minimum and demonstrates all computations using SAS. Most of the programs and output are displayed in a self-contained way, with an emphasis on the interpretation of the output in terms of how it relates to the motivating example. Plenty of exercises conclude every chapter. All of the datasets and SAS programs are available from the book's website, along with other ancillary material.
- Minimum of mathematics and equation knowledge required
- Emphasis on interpretation and use of statistical methods, with many examples from current events
- Use of the computer language SAS with a minimal knowledge of SAS needed
- All of the datasets and SAS programs are available from the book's website along with other ancillary material
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Product details
- Date Published: July 2010
- format: Hardback
- isbn: 9780521761598
- length: 288 pages
- dimensions: 262 x 182 x 21 mm
- weight: 0.67kg
- contains: 69 b/w illus. 104 tables 118 exercises
- availability: Available
Table of Contents
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 tables.
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Author
Daniel Zelterman, Yale University, Connecticut
Dr Daniel Zelterman is Professor of Epidemiology and Public Health in the Division of Biostatistics at Yale University. His application areas include work in genetics, HIV, and cancer. Before moving to Yale in 1995, he was on the faculty of the University of Minnesota and at the State University of New York at Albany. He is an elected Fellow of the American Statistical Association. He serves as associate editor of Biometrics and other statistical journals. He is the author of Models for Discrete Data (1999), Advanced Log-Linear Models Using SAS (2002), Discrete Distributions: Application in the Health Sciences (2004), and Models for Discrete Data, 2nd edition (2006).