Statistics for the Life Sciences, Fourth Edition, covers the key concepts of statistics as applied to the life sciences, while incorporating the tools and themes of modern data analysis. This text uses an abundance of real data in the exercises and examples, and minimizes computation, so that readers can focus on the statistical concepts and issues, not the mathematics. Basic algebra is assumed as a prerequisite.
Jeff Witmer is a PhD statistician and Professor at Oberlin College. He is a Fellow of the American Statistical Association, previous Chair of the Section on Statistical Education of ASA, previous editor of STATS magazine, and a member of the editorial board of the Consortium for the Advancement of Undergraduate Statistics Education. His work has been supported by grants from the National Science Foundation and the National Institutes of Health.
Andrew Schaffner is a PhD statistician and Professor at California Polytechnic State University—San Luis Obispo. Additionally, he serves as the staff research statistician for both the Cal Poly Environmental Biotechnology Institute and the Morro Bay National Estuary Program. Organizations that support his research include the National Institutes of Health, United States Department of Agriculture, and the US Environmental Protection Agency.
2. Description of Samples and Populations
3. Probability, and the Binomial Distribution
4. The Normal Distribution
5. Sampling Distributions
6. Confidence Intervals
7. Comparison of Two Independent Samples
8. Comparison of Paired Samples
9: Categorical Data: One-Sample Distributions
10. Categorical Data: Relationships
11. Comparing the Means of Many Independent Samples
12. Linear Regression and Correlation
13. A Summary of Inference Methods