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
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Editions-Typ
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Illustrationen
Worked examples or Exercises
Maße
Höhe: 251 mm
Breite: 177 mm
Dicke: 22 mm
Gewicht
ISBN-13
978-1-108-47818-2 (9781108478182)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Daniel Zelterman, PhD, is Professor Emeritus, Department of Biostatistics, at Yale University. His application areas include work in clinical trial designs for cancer studies. 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. In his spare time he plays oboe and bassoon and has backpacked hundreds of miles of the Appalachian Trail.
Autor*in
Yale University, Connecticut
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.
