Statistics for the Life Sciences - Hardcover

Samuels, Myra L.

Hardcover

ISBN 10: 0024055018 ISBN 13: 9780024055019

Publisher: Macmillan USA, 1989

Synopsis

For graduate or undergraduate courses in biology, agronomy, medical and health sciences, nutrition, pharmacy, animal science, physical education, forestry, and other life sciences.

With a strong emphasis on real data, exploratory data analysis, interpretation of results, and checking assumptions, this text clearly conveys the key concepts of statistics as applied to life sciences while incorporating tools and themes of modern data analysis. The authors' goal is to help students understand concepts not memorize formulas; they make liberal use of exercises, worked examples, and graphical methods to do so.

About this title

About the Author:

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.

Synopsis:

This text aims to enable students to execute elementary statistical analyses and interpret the results. It uses real data from biology, health sciences, pharmacy and agricculture. It explains descriptive statistics, inferential statistics and basic design using, the language of the life scientist. A discussion of the relation of the random-sampling model to real biological investigations illustrates how the sample population construct can be applied to biological data. Students are introduced to the pitfalls of data dredging and the advantages of focused analysis in a chapter on multiple inference. Linear combinations in ANOVA are discussed to introduce students to concepts of statistical adjustment and interaction and a thorough discussion of sampling distributions helps students to grasp the basic ideas of inference.

About this edition

• Publisher: Macmillan USA
• Publication date: 1989
• ISBN 10: 0024055018
• ISBN 13: 9780024055019
• Binding: Hardcover
• Number of pages: 610