Cause and Correlation in Biology

A User's Guide to Path Analysis, Structural Equations and Causal Inference with R

Nonfiction, Science & Nature, Science, Biological Sciences, Ecology, Mathematics, Nature
Cover of the book Cause and Correlation in Biology by Bill Shipley, Cambridge University Press
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Author: Bill Shipley ISBN: 9781316537879
Publisher: Cambridge University Press Publication: April 18, 2016
Imprint: Cambridge University Press Language: English
Author: Bill Shipley
ISBN: 9781316537879
Publisher: Cambridge University Press
Publication: April 18, 2016
Imprint: Cambridge University Press
Language: English

Many problems in biology require an understanding of the relationships among variables in a multivariate causal context. Exploring such cause-effect relationships through a series of statistical methods, this book explains how to test causal hypotheses when randomised experiments cannot be performed. This completely revised and updated edition features detailed explanations for carrying out statistical methods using the popular and freely available R statistical language. Sections on d-sep tests, latent constructs that are common in biology, missing values, phylogenetic constraints, and multilevel models are also an important feature of this new edition. Written for biologists and using a minimum of statistical jargon, the concept of testing multivariate causal hypotheses using structural equations and path analysis is demystified. Assuming only a basic understanding of statistical analysis, this new edition is a valuable resource for both students and practising biologists.

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Many problems in biology require an understanding of the relationships among variables in a multivariate causal context. Exploring such cause-effect relationships through a series of statistical methods, this book explains how to test causal hypotheses when randomised experiments cannot be performed. This completely revised and updated edition features detailed explanations for carrying out statistical methods using the popular and freely available R statistical language. Sections on d-sep tests, latent constructs that are common in biology, missing values, phylogenetic constraints, and multilevel models are also an important feature of this new edition. Written for biologists and using a minimum of statistical jargon, the concept of testing multivariate causal hypotheses using structural equations and path analysis is demystified. Assuming only a basic understanding of statistical analysis, this new edition is a valuable resource for both students and practising biologists.

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