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A Comprehensive Guide to Factorial Two-Level Experimentation 2009 Edition
Contributor(s): Mee, Robert (Author)
ISBN: 1489982701     ISBN-13: 9781489982704
Publisher: Springer
OUR PRICE:   $113.99  
Product Type: Paperback - Other Formats
Published: September 2014
Qty:
Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - General
- Science | Chemistry - General
- Technology & Engineering | Materials Science - General
Dewey: 519.5
Physical Information: 1.15" H x 6.14" W x 9.21" (1.73 lbs) 545 pages
 
Descriptions, Reviews, Etc.
Publisher Description:

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, and 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.

"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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