Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance

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Overview

Ecological theories and hypotheses are usually complex because of natural variability in space and time, which often makes the design of experiments difficult. This book describes how to design ecological experiments from a statistical basis using analysis of variance, so reliable conclusions can be drawn. The logical procedures that lead to a need for experiments are described, followed by an introduction to simple statistical tests. This leads to a detailed account of analysis of variance, looking at procedures, assumptions, and problems. One-factor analysis is extended to nested (hierarchical) designs and factorial analysis. Finally, some regression methods for examining relationships between variables are covered. Examples of ecological experiments are used throughout to illustrate the procedures and examine problems. This book will be invaluable to practicing ecologists as well as advanced students involved in experimental design.
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Editorial Reviews

From the Publisher
"...this book is important for ecologists to read...We do however need the methods of statistical inference to help sort out the details of ecological problems, and Underwood's important contribution in this book is to help convince us that the ecological devil is in the details." Charles J. Krebs, Revue Des Livres

"There are some valuable insights contained in the book that would aid those involved in spatial and temporal studies of landscapes and biological responses of different systems. The regression approaches to analyze the relationships among variables are quite detailed and insightful. It serves as a good reference on analysis of variance...." J. Environ. Qual

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Product Details

  • ISBN-13: 9780521553292
  • Publisher: Cambridge University Press
  • Publication date: 12/12/1996
  • Pages: 524
  • Product dimensions: 5.98 (w) x 8.98 (h) x 1.30 (d)

Table of Contents

1. Introduction; 2. A framework for investigating biological patterns and processes; 3. Populations, frequency distributions and samples; 4. Statistical tests of null hypotheses; 5. Statistical tests on samples; 6. Simple experiments comparing the means of two populations; 7. Analysis of variance; 8. More analysis of variance; 9. Nested analyses of variance; 10. Factorial experiments; 11. Construction of any analysis from general principles; 12. Some common and some particular experimental designs; 13. Analysis involving relationships among variables; 14. Conclusions: where to from here?
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