A Comprehensive Guide to Factorial Two-Level Experimentation

Factorial designs enable researchers to experiment with many factors. The 50 published examples re-analyzed in this guide attest to the prolific use of two-level factorial designs. As a testimony to this universal applicability, the examples come from diverse fields: Analytical Chemistry Animal Scie...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Mee, Robert (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Temas:
Statistics .
Chemistry.
Materials science.
Industrial engineering.
Production engineering.
Mechanical engineering.
Marketing.
Statistical Theory and Methods.
Materials Science.
Industrial and Production Engineering.
Mechanical Engineering.
Acceso en línea:Texto Completo

MARC

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505 0 |a Full Factorial Designs -- to Full Factorial Designs with Two-Level Factors -- Analysis of Full Factorial Experiments -- Common Randomization Restrictions -- More Full Factorial Design Examples -- Fractional Factorial Designs -- Fractional Factorial Designs: The Basics -- Fractional Factorial Designs for Estimating Main Effects -- Designs for Estimating Main Effects and Some Two-Factor Interactions -- Resolution V Fractional Factorial Designs -- Augmenting Fractional Factorial Designs -- Fractional Factorial Designs with Randomization Restrictions -- More Fractional Factorial Design Examples -- Additional Topics -- Response Surface Methods and Second-Order Designs -- Special Topics Regarding the Design -- Special Topics Regarding the Analysis -- Appendices and Tables -- Upper Percentiles of t Distributions, t -- Upper Percentiles of F Distributions, F -- Upper Percentiles for Lenth t Statistics, and -- Computing Upper Percentiles for Maximum Studentized Residual -- Orthogonal Blocking for Full 2 Factorial Designs -- Column Labels of Generators for Regular Fractional Factorial Designs -- Tables of Minimum Aberration Regular Fractional Factorial Designs -- Minimum Aberration Blocking Schemes for Fractional Factorial Designs -- Alias Matrix Derivation -- Distinguishing Among Fractional Factorial Designs. 
520 |a Factorial designs enable researchers to experiment with many factors. The 50 published examples re-analyzed in this guide attest to the prolific use of two-level factorial designs. As a testimony to this universal applicability, the examples come from diverse fields: Analytical Chemistry Animal Science Automotive Manufacturing Ceramics and Coatings Chromatography Electroplating Food Technology Injection Molding Marketing Microarray Processing Modeling and Neural Networks Organic Chemistry Product Testing Quality Improvement Semiconductor Manufacturing Transportation Focusing on factorial experimentation with two-level factors makes this book unique, allowing the only comprehensive coverage of two-level design construction and analysis. Furthermore, since two-level factorial experiments are easily analyzed using multiple regression models, this focus on two-level designs makes the material understandable to a wide audience. This book is accessible to non-statisticians having a grasp of least squares estimation for multiple regression and exposure to analysis of variance. Robert W. Mee is Professor of Statistics at the University of Tennessee. Dr. Mee is a Fellow of the American Statistical Association. He has served on the Journal of Quality Technology (JQT) Editorial Review Board and as Associate Editor for Technometrics. He received the 2004 Lloyd Nelson award, which recognizes the year's best article for practitioners in JQT. "This book contains a wealth of information, including recent results on the design of two-level factorials and various aspects of analysis... The examples are particularly clear and insightful." (William Notz, Ohio State University "One of the strongest points of this book for an audience of practitioners is the excellent collection of published experiments, some of which didn't 'come out' as expected... A statistically literate non-statistician who deals with experimental design will have plenty of motivation to read this book, and the payback for the effort will be substantial." (Max Morris, Iowa State University). 
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650 0 |a Materials science. 
650 0 |a Industrial engineering. 
650 0 |a Production engineering. 
650 0 |a Mechanical engineering. 
650 0 |a Marketing. 
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650 2 4 |a Materials Science. 
650 2 4 |a Industrial and Production Engineering. 
650 2 4 |a Mechanical Engineering. 
650 2 4 |a Marketing. 
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