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200 10$aCause and correlation in biology $ea user's guide to path analysis, structural equations, and causal inference /$fBill Shipley
205   $a1st ed.
210   $aCambridge, UK ;$aNew York, NY. USA $cCambridge University Press$d2000
215   $a1 online resource (xii, 317 pages) $cdigital, PDF file(s)
300   $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
311 08$a0-521-52921-2 
311 08$a0-521-79153-7 
320   $aIncludes bibliographical references (p. 308-315) and index.
327   $aCover; 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
330   $aThis 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.
606   $aBiometry
615  0$aBiometry.
676   $a570/.1/5195
700   $aShipley$b Bill$f1960-$01837043
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910965155803321
996   $aCause and correlation in biology$94415365
997   $aUNINA