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Cause and Correlation in Biology: A User's Guide to Path Analysis, Structural Equations and Causal Inference ペーパーバック – 2002/8/1
英語版
Bill Shipley
(著)
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 randomised 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 minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.
- 本の長さ332ページ
- 言語英語
- 出版社Cambridge University Press
- 発売日2002/8/1
- 寸法15.24 x 1.91 x 22.23 cm
- ISBN-100521529212
- ISBN-13978-0521529211
商品の説明
レビュー
'… the perfect introduction to SEM. This book can be used as the primary text in a SEM course given within any discipline, and can be used by scholars and researchers from any area of science.' Structural Equation Modeling
'Addressing students and practising biologists, Shipley does a terrific job of making mathematical ideas accessible … Cause and Correlation in Biology is a nontechnical and honest introduction to statistical methods for testing causal hypotheses.' Johan Paulsson, Nature Cell Biology
'I highly recommend the book for those interested in multivariate approaches to biology.' Annals of Botany
'I highly recommend the book by Shipley for those interested in multivariate approaches to biology.' Annals of Botany
'Addressing students and practising biologists, Shipley does a terrific job of making mathematical ideas accessible … Cause and Correlation in Biology is a nontechnical and honest introduction to statistical methods for testing causal hypotheses.' Johan Paulsson, Nature Cell Biology
'I highly recommend the book for those interested in multivariate approaches to biology.' Annals of Botany
'I highly recommend the book by Shipley for those interested in multivariate approaches to biology.' Annals of Botany
著者について
Bill Shipley teaches plant ecology and biometry in the Department of Biology at the Universite de Sherbrooke, Canada.
登録情報
- 出版社 : Cambridge University Press (2002/8/1)
- 発売日 : 2002/8/1
- 言語 : 英語
- ペーパーバック : 332ページ
- ISBN-10 : 0521529212
- ISBN-13 : 978-0521529211
- 寸法 : 15.24 x 1.91 x 22.23 cm
- カスタマーレビュー:
著者について
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カスタマーレビュー
星5つ中4.8つ
5つのうち4.8つ
全体的な星の数と星別のパーセンテージの内訳を計算するにあたり、単純平均は使用されていません。当システムでは、レビューがどの程度新しいか、レビュー担当者がAmazonで購入したかどうかなど、特定の要素をより重視しています。 詳細はこちら
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他の国からのトップレビュー
Eli Rose
5つ星のうち5.0
Good coverage of the topic
2017年1月2日にアメリカ合衆国でレビュー済みAmazonで購入
Very well written and engaging. Good coverage of the topic.
Zac
5つ星のうち4.0
Very good introduction for practitioner.
2006年1月3日にアメリカ合衆国でレビュー済みAmazonで購入
The book of Shipley provides an easy to read introduction in the field of structural equations and causal inference from experimental data. Most concepts are very well explained and examples are provided to see how to apply the concepts practically. Without any reservation I recommend it to none experts in this field who want to learn the intermediate basics directly applicable to own problems, e.g., for biologists.
However, for people trained in more theoretical fields, e.g., computer science, the thorough verbal explanations of mathematical formular (which are provided) is a little tiring after the first 100 pages, because the relative simple formulas can easily be interpreted. Instead, I wished the book would provide more alternative approaches in form of statistical test to test for causality. For this reason, I would recommend not to rewrite the complete book, but only include one additional chapter written in a more technical style. Then it would be close to perfect.
Finally, I want to remark, that the historical notes given throughout the book are not only very intesting but also inspiring, because they remind not to oversee mathematical developments in the none mathematical literature. SEM is certainly a good example.
To summarize, this is really a decent book!
However, for people trained in more theoretical fields, e.g., computer science, the thorough verbal explanations of mathematical formular (which are provided) is a little tiring after the first 100 pages, because the relative simple formulas can easily be interpreted. Instead, I wished the book would provide more alternative approaches in form of statistical test to test for causality. For this reason, I would recommend not to rewrite the complete book, but only include one additional chapter written in a more technical style. Then it would be close to perfect.
Finally, I want to remark, that the historical notes given throughout the book are not only very intesting but also inspiring, because they remind not to oversee mathematical developments in the none mathematical literature. SEM is certainly a good example.
To summarize, this is really a decent book!