Design of Comparative Experiments (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 25)

This book should be on the shelf of every practising statistician who designs experiments. Good design considers units and treatments first, and then allocates treatments to units. It does not choose from a menu of named designs. This approach requires a notation for units that does not depend on the treatments applied. Most structure on the set of observational units, or on the set of treatments, can be defined by factors. This book develops a coherent framework for thinking about factors and their relationships, including the use of Hasse diagrams. These are used to elucidate structure, calculate degrees of freedom and allocate treatment subspaces to appropriate strata. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses. Examples, exercises and discussion questions are drawn from a wide range of real applications: from drug development, to agriculture, to manufacturing. Good Design Considers Units And Treatments First, And Then Allocates Treatments To Units. It Does Not Choose From A Menu Of Named Designs. This Approach Requires A Notation For Units That Does Not Depend On The Treatments Applied. Most Structure On The Set Of Observational Units, Or On The Set Of Treatments, Can Be Defined By Factors. This Book Develops A Coherent Framework For Thinking About Factors And Their Relationships, Including The Use Of Hasse Diagrams. These Are Used To Elucidate Structure, Calculate Degrees Of Freedom And Allocate Treatment Subspaces To Appropriate Strata. Based On A One-term Course The Author Has Taught Since 1989, The Book Is Ideal For Advanced Undergraduate And Beginning Graduate Courses. Examples, Exercises And Discussion Questions Are Drawn From A Wide Range Of Real Applications: From Drug Development, To Agriculture, To Manufacturing.--jacket. Forward Look -- Unstructured Experiments -- Simple Treatment Structure -- Blocking -- Factorial Treatment Structure -- Row-column Designs -- Experiments On People And Animals -- Small Units Inside Large Units -- More About Latin Squares -- The Calculus Of Factors -- Incomplete-block Designs -- Factorial Designs In Incomplete Blocks -- Fractional Factorial Designs -- Backward Look. R.a. Bailey. Includes Bibliographical References (p. 321-326) And Index. Design of Comparative Experiments develops a coherent framework for thinking about factors that affect experiments and their relationships, including the use of Hasse diagrams. These diagrams are used to elucidate structure, calculate degrees of freedom and allocate treatment sub-spaces to appropriate strata. Good design considers units and treatments first, and then allocates treatments to units. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses. This book should be on the shelf of every practicing statistician who designs experiments.