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MARC 21

A Comprehensive Guide to Factorial Two-Level Experimentation
Tag	Description
020	$a9780387891033
082	$a519.5
099	$aOnline Resource : Springer
100	$aMee, Robert.
245	$aA Comprehensive Guide to Factorial Two-Level Experimentation$h[Ebook]$cby Robert Mee.
260	$aNew York, NY$bSpringer$c2009.
300	$aXVIII, 550 pages$bonline resource.
336	$atext
338	$aonline resource
505	$aIntroduction to full factorial designs with two-level factors -- Analysis of full factorial experiments -- Common randomization restrictions -- More full factorial design examples -- 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 designs with randomization restrictions -- More fractional factorial design examples -- Response surface methods and second-order designs -- Special topics regarding the design -- Special topics regarding the analysis.
520	$aFactorial 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 yearâs 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 analysisâ¦ 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 didnât âcome outâ as expectedâ¦ 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)
538	$aOnline access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
710	$aSpringerLink (Online service)
856	$uhttp://dx.doi.org/10.1007/b105081