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Regression for Health and Social Science : Applied Linear Models with R by Daniel Zelterman (2022, Hardcover)
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-101108478182
ISBN-139781108478182
eBay Product ID (ePID)24057246667
Product Key Features
Number of Pages350 Pages
Publication NameRegression for Health and Social Science : Applied Linear Models with R
LanguageEnglish
Publication Year2022
SubjectBiostatistics, Probability & Statistics / General
FeaturesNew Edition
TypeTextbook
AuthorDaniel Zelterman
Subject AreaMathematics, Medical
FormatHardcover
Dimensions
Item Height0.7 in
Item Length9.8 in
Item Width6.9 in
Additional Product Features
Intended AudienceCollege Audience
LCCN2022-009235
Dewey Edition23
IllustratedYes
Dewey Decimal300.1519536
Table Of ContentPreface; 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.
Edition DescriptionNew Edition
SynopsisUsing every-day examples and numerous exercises, this text covers the basics of linear models with a minimum of mathematics. The emphasis is on issues involved in the analysis and the interpretation of computer output. R code is provided and explained allowing readers to apply the methods to their own data., 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
LC Classification NumberHA31.3
