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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 Web site, along with other ancillary material.Read more
- 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
Reviews & endorsements
"The author is an established and immensely experienced biostatistical and statistical researcher, and an excellent educator and speaker. His skill in teaching and presentation is very obvious in the engaging, sometime humorous, and easily comprehensible presentation style of the book. I will definitely keep a copy of this book on hand for my nonstatistical collaborators and even undergraduate students interested in a lucid yet thorough introduction to day-to-day biostatistical methods and applied data analysis."
Debajyoti Sinha, The American Statistician
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- Date Published: May 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
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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Instructors have used or reviewed this title for the following courses
- Applied Data Analysis in Public Health Lab 2
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- Applied Linear Models
- Applied Statistics using SAS
- Current Biostatistical Methods and Computational Applications
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