Cause and Correlation in Biology. A User's Guide to Path Analysis, Structural Equations and Causal Inference (Relié)

Bill Shipley

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  • Cambridge University Press

  • Paru le : 14/04/2001
  • 1 million de livres à découvrir
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This book goes beyond the truism that "correlation does not imply causation" and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomized or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimizes technical jargon, the book is aimed at practicing biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.
    • Preliminaries
    • From cause to correlation and back
    • Sewall Wright, path analysis and d-separation
    • Path analysis and maximum likelihood
    • Measurement error and latent variables
    • The structural equations model
    • Nested models and multilevel models
    • Exploration, discovery and equivalence
  • Date de parution : 14/04/2001
  • Editeur : Cambridge University Press
  • ISBN : 0-521-79153-7
  • EAN : 9780521791533
  • Présentation : Relié
  • Nb. de pages : 320 pages
  • Poids : 0.665 Kg
  • Dimensions : 16,0 cm × 23,5 cm × 1,9 cm

Biographie de Bill Shipley

Bill Shipley teaches plant ecology and biometry in the Department of Biology at the Université de Sherbrooke, Quebec, Canada. His present ecological research concentrates on comparative ecophysiology and the ways in which plant attributes interact to produce ecological outcomes. He has also contributed significantly to research in topics including plant competition, species richness and plant community ecology. His statistical research is equally diverse, covering areas such as permutation and bootstrap methods, path analysis, dynamic game theory and non-parametric regression smoothers. This rare combination of practical experience in both experimental science and statistical research makes him well positioned to communicate statistical methods to practicing biologists in a meaningful way.

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