Primer of Applied Regression & Analysis of Variance

Rating:
Summary: The best advanced statistics book for biologists
Review: Like all advanced stats books, this one has mathematical rigor and plenty of examples. But unlike the others, this one is written from the point of view of a biologist. You won't just learn the math, you'll learn how to make sense of the results. 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 to learn about various kinds of ANOVAS and regressions.

Rating:
Summary: The best second book of statistics for biologists.
Review: 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.