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Experimental Design and Data Analysis for Biologists

Experimental Design and Data Analysis for Biologists - 02 edition

ISBN13: 978-0521811286

Cover of Experimental Design and Data Analysis for Biologists 02 (ISBN 978-0521811286)
ISBN13: 978-0521811286
ISBN10: 0521811287
Cover type:
Edition/Copyright: 02
Publisher: Cambridge University Press
Published: 2002
International: No
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Experimental Design and Data Analysis for Biologists - 02 edition

ISBN13: 978-0521811286

Gerry P. Quinn and Michael J. Keough

ISBN13: 978-0521811286
ISBN10: 0521811287
Cover type:
Edition/Copyright: 02
Publisher: Cambridge University Press

Published: 2002
International: No
Summary

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

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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