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Experimental Design and Data Analysis for Biologists

Experimental Design and Data Analysis for Biologists - 02 edition

Experimental Design and Data Analysis for Biologists - 02 edition

ISBN13: 9780521009768

ISBN10: 0521009766

Experimental Design and Data Analysis for Biologists by Gerry P. Quinn and Michael J. Keough - ISBN 9780521009768
Cover type: Paperback
Edition: 02
Copyright: 2002
Publisher: Cambridge University Press
International: No
Experimental Design and Data Analysis for Biologists by Gerry P. Quinn and Michael J. Keough - ISBN 9780521009768

ISBN13: 9780521009768

ISBN10: 0521009766

Cover type: Paperback
Edition: 02

List price: $110.00

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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.

Table of Contents

Table of Contents

1. Introduction
2. Estimation
3. Hypothesis testing
4. Graphical exploration of data
5. Correlation and regression
6. Multiple regression and correlation
7. Design and power analysis
8. Comparing groups or treatments - analysis of variance
9. Multifactor analysis of variance
10. Randomized blocks and simple repeated measures: unreplicated two-factor designs
11. Split plot and repeated measures designs: partly nested anovas
12. Analysis of covariance
13. Generalized linear models and logistic regression
14. Analyzing frequencies
15. Introduction to multivariate analyses
16. Multivariate analysis of variance and discriminant analysis
17. Principal components and correspondence analysis
18. Multidimensional scaling and cluster analysis
19. Presentation of results.

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