Experimental Design and Data Analysis for Biologists

Name: Experimental Design and Data Analysis for Biologists
Price: 16.88 USD
Availability: InStock
ISBN: 9780521009768

ISBN-10: 0521009766

ISBN-13: 9780521009768

Edition: 2002

Authors: Michael J. Keough, Gerry P. Quinn

List price: $73.99

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Description:

This essential textbook is designed for students or researchers in biology who need to design experiments, sampling programs, or analyze resulting data. The text begins with a revision of estimation and hypothesis testing methods, before advancing to the analysis of linear and generalized linear models. The chapters include such topics as linear and logistic regression, simple and complex ANOVA models, log-linear models, and multivariate techniques. The main analyses are illustrated with many examples from published papers and an extensive reference list to both the statistical and biological literature is also included. The book is supported by a web-site that provides all data sets,…

Book details

List price: $73.99
Copyright year: 2002
Publisher: Cambridge University Press
Publication date: 3/21/2002
Binding: Paperback
Pages: 553
Size: 7.48" wide x 9.69" long x 1.18" tall
Weight: 3.190
Language: English

Michael Keough is a Reader in Zoology at the University of Melbourne. His research interests include the ecology of natural and human-induced disturbances in coastal habitats. He is co-author of Experimental Design and Data Analysis for Biologists, Cambridge University Press, 2002.




Introduction



Estimation



Hypothesis testing



Graphical exploration of data



Correlation and regression



Multiple regression and correlation



Design and power analysis



Comparing groups or treatments - analysis of variance



Multifactor analysis of variance



Randomized blocks and simple repeated measures: unreplicated two-factor designs



Split plot and repeated measures designs: partly nested anovas



Analysis of covariance



Generalized linear models and logistic regression



Analyzing frequencies



Introduction to multivariate analyses



Multivariate analysis of variance and discriminant analysis



Principal components and correspondence analysis



Multidimensional scaling and cluster analysis



Presentation of results