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Applicable for all statistics courses or practical use, teaches how to understand more advanced multivariate statistical methods, as well as how to use available software packages to get correct results. Study problems and examples culled from biomedical research illustrate key points. New to this edition: broadened coverage of ANOVA (traditional analysis of variance), the addition of ANCOVA (analysis of Co-Variance); updated treatment of available statistics software; 2 new chapters (Analysis of Variance Extensions and Mixing Regression and ANOVA: ANCOVA).
- Sales Rank: #544099 in Books
- Published on: 2000-11-15
- Original language: English
- Number of items: 1
- Dimensions: 9.70" h x 1.70" w x 7.90" l, 3.55 pounds
- Binding: Hardcover
- 949 pages
Review
This is a solid reference work. Biostatisticians and epidemiologists will find it useful. 3 Stars."--"Doody's Review Service,"
From the Back Cover
Demystifies the Use of Advanced Statistcal Methods
Unlike other texts, Primer of Applied Regression & Analysis of Variance teaches both how to understand more advanced multivariate statistical methods, as well as how to use statistical software to get the correct results. This new edition offers the modern, intuitive approaches that won the first edition a wide following, while adding traditional methods for complete coverage of applied statistical methods.
FEATURES:
*Reader-friendly style that makes complicated material approachable and usable
*Practical guidelines for the correct application of statistical software
*Examples from biological and health sciences research that clarify key points
*End-of-chapter study problems that quickly test mastery of the material NEW IN THIS EDITION
*Expanded coverage of traditional ANOVA (analysis of variance)
*Expanded coverage of ANOVA extensions, assumptions, and workarounds for "problem" data
*Cox proportional hazard models
*Expanded coverage of repeated measures
*New examples from biological and health sciences research
*Expanded and revised coverage of statistical software
*Web site (http:www.vetmed.wsu.edu/AppliedRegression/) to support statistics instruction and facilitate use of example and problem data sets
About the Author
Stanton Glantz is Professor of Medicine and Director of the Center for Tobacco Control Research and Education at the University of California San Francisco, where he conducts research on tobacco control and cardiology. He is author or co-author of over 300 scientific papers and nine books in addition to Primer of Biostatistics, 7th ed. (McGraw-Hill, 2012) and Primer of Applied Regression & Analysis of Variance, 2nd ed. (McGraw-Hill, 2001). He wrote the first major review (published in Circulation) which identified involuntary smoking as a cause of heart disease, and the landmark July 19, 1995 issue of JAMA on the Brown and Williamson documents, which showed that the tobacco industry knew 30 years ago that nicotine is addictive and that smoking causes cancer. His work has attracted considerable attention from the tobacco industry, which has sued the University of California twice (unsuccessfully) in an effort to stop Professor Glantz's work.
Most helpful customer reviews
15 of 16 people found the following review helpful.
The best second book of statistics for biologists.
By Harvey Motulsky
Once you've learned the basic principles of statistics, how can a biologist learn more advanced techniques? Many books focus on math rather than on understanding concepts. Other books are too narrow -- discussing only a single method. And books that focus on multiple regression and ANOVA tend to have examples from psychology and social sciences. Glantz and Slinker do a great job of explaining the principles of multiple regression, analysis of variance, and analysis of covariance. The focus is not on mathematical proofs, but rather on making sense of the results in the context of biological and medical research.
This book also has excellent chapters on linear regression, nonlinear regression (curve fitting) and logistic and proportional hazards regression (regression when the outcome is an either-or binary variable).
New to the second edition are a chapter on analysis of covariance, more extensive discussions of multiple comparisons methods, and a discussion of Cox proportional hazards regression for analyses of survival data.
The title is a bit misleading. This is not a "primer" of statistics. But once you've learned the basic principles of statistics, this is THE book for biologists to learn about various kinds of ANOVAS and regressions.
3 of 3 people found the following review helpful.
Outstanding
By Brant Inman
I looked at several options for a regression textbook that would be both understandable and relatively complete for my introduction to the topic. This book won hands down. The authors keep it simple and use a wide variety of examples to get the point across. I felt that the sections on logistic and Cox regression could have been a bit better, but these subjects are best learned by dedicated textbooks such as Hosmer and Lemeshow and Collett.
I think that this may be the best introductory regression book out there.
1 of 1 people found the following review helpful.
good
By S. Rowe
This is a very good tool for learning regression and ANOVA. ANOVA illustrated via regression - a far more sensible approach than many other intro stats books. Thus, the presentation is highly visual. At every turn, the author presents lovely citations for a deeper investigation of the concepts. The book itself is a good start, all while recommending resources should curiosity warrant.
However, there is a HORRIBLE mistake on page 344. See Kutner et alias' "Applied Linear Statistical models," pg 841, for the correction. Additionally, the author seems to present Type I sums of squares, whereas Type III has proven (more?) popular and from this reviewers perspective is more sensible.
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