Experimental Design and Data Analysis for Biologists

ISBN-10: 0521009766
ISBN-13: 9780521009768
Edition: 2002
List price: $110.00 Buy it from $68.48
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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  More...

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Book details

List price: $110.00
Copyright year: 2002
Publisher: Cambridge University Press
Publication date: 3/21/2002
Binding: Paperback
Pages: 553
Size: 7.25" wide x 9.25" long x 1.25" tall
Weight: 3.366
Language: English

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, questions for each chapter and links to software.

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

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