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Design and Analysis of Ecological Experiments [Paperback]

Samuel M. Scheiner (Editor), Jessica Gurevitch (Editor)
4.0 out of 5 stars  See all reviews (1 customer review)

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Book Description

April 26, 2001 0195131886 978-0195131888 2
Ecological research and the way that ecologists use statistics continues to change rapidly. This second edition of the best-selling Design and Analysis of Ecological Experiments leads these trends with an update of this now-standard reference book, with a discussion of the latest developments in experimental ecology and statistical practice.
The goal of this volume is to encourage the correct use of some of the more well known statistical techniques and to make some of the less well known but potentially very useful techniques available. Chapters from the first edition have been substantially revised and new chapters have been added. Readers are introduced to statistical techniques that may be unfamiliar to many ecologists, including power analysis, logistic regression, randomization tests and empirical Bayesian analysis. In addition, a strong foundation is laid in more established statistical techniques in ecology including exploratory data analysis, spatial statistics, path analysis and meta-analysis. Each technique is presented in the context of resolving an ecological issue. Anyone from graduate students to established research ecologists will find a great deal of new practical and useful information in this current edition.

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Design and Analysis of Ecological Experiments + A Primer Of Ecological Statistics + Experimental Design and Data Analysis for Biologists
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Editorial Reviews

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"The 18 chapters in this graduate textbook on advanced statistical techniques for ecologists describe such methods as power analysis, logistic regression, randomization tests, and empirical Bayesian analysis. The second edition reflects changes in statistical theory, and computer software and hardware capabilities."--SciTech Book News


About the Author

Samuel M. Scheiner is at Arizona State University. Jessica Gurevitch is at S.U.N.Y. at Stony Brook.

Product Details

  • Paperback: 432 pages
  • Publisher: Oxford University Press, USA; 2 edition (April 26, 2001)
  • Language: English
  • ISBN-10: 0195131886
  • ISBN-13: 978-0195131888
  • Product Dimensions: 9 x 6 x 1.2 inches
  • Shipping Weight: 1.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #638,303 in Books (See Top 100 in Books)

 

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4.0 out of 5 stars A through revision of methods, April 9, 2008
This review is from: Design and Analysis of Ecological Experiments (Paperback)
This book indeed is useful for what it was written for. Easy enough language, yet detailed. I would like, however, that the dataset downloading website actually worked.
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Inside This Book (learn more)
First Sentence:
Ecology is more and more an experimental science. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
proportion fruit set, variable distance matrix, prospective power analyses, individually grown plants, classified quantitative data, absolute effect size, studentized bootstrap, censored data points, associate species, retrospective power analysis, nectar production rate, detectable effect size, focal species, true effect size, marine producers, ant removal, treatment mean squares, individual response variables, parametric variance, prey depletion, host chemistry, biomass experiment, spatial methods, bootstrap distribution, categorical response variable
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Institute Inc, Storke Road, Jessica Gurevitch, Coal Oil Point, Black Spanish, Rapid Cycling, Campus Point, Little Rock Lake, Grapevine Hills, Sam Schemer, Ver Hoef, Monte Carlo, Error Lower Upper, Nonlinear Least-Squares Summary Statistics Dependent Variable, Squares Mean Square Regression, Uncorrected Total, Asymptotic Confidence Interval Parameter Estimate Std, Plant Nutrient, Santa Barbara, Sum of Source, Unreplicated Large-scale Experiments
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