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Cause and Correlation in Biology :A ...

Ebooks Corporation.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Cause and Correlation in Biology :
其他題名:	A User's Guide to Path Analysis, Structural Equations and Causal Inference.
作者:	Shipley, Bill.
出版者:	Cambridge :Cambridge University Press,2000.
面頁冊數:	329 p.
標題:	Biometry.
電子資源:	Click here to view book
ISBN:	9780511605949 (electronic bk.)

Cause and Correlation in Biology :A User's Guide to Path Analysis, Structural Equations and Causal Inference.
Shipley, Bill.

Cause and Correlation in Biology :A User's Guide to Path Analysis, Structural Equations and Causal Inference.[electronic resource]. - Cambridge :Cambridge University Press,2000. - 329 p.

Cover; Half-title; Title; Copyright; Dedication; Contents; Preface; 1 Preliminaries; 2 From cause to correlation and back; 3 Sewall Wright, path analysis and d-separation; 4 Path analysis and maximum likelihood; 5 Measurement error and latent variables; 6 The structural equations model; 7 Nested models and multilevel models; 8 Exploration, discovery and equivalence; Appendix; References; Index

Written in a jargon-free, conversational style, this book explores the relationship 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 controlled experiments.

Electronic reproduction.

Available via World Wide Web.

Mode of access: World Wide Web.

ISBN: 9780511605949 (electronic bk.)Subjects--Topical Terms:

182039
Biometry.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: QH323.5

Dewey Class. No.: 570/.1/5195

Cause and Correlation in Biology :A User's Guide to Path Analysis, Structural Equations and Causal Inference.
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520 $a Written in a jargon-free, conversational style, this book explores the relationship 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 controlled experiments.
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