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Modern experimental design

Author: Thomas P Ryan; Wiley InterScience (Online service)
Publisher: Hoboken, N.J. : Wiley-Interscience, ©2007.
Series: Wiley series in probability and statistics.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Suitable for use as either an undergraduate or graduate text, depending on the audience, this book covers a wide range of topics, including some that have received increased attention in recent years, such as hard-to-change factors, uniform designs, multiple response optimization, and Analysis of Means (ANOM).
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Genre/Form: Electronic books
Textbooks
Additional Physical Format: Print version:
Ryan, Thomas P., 1945-
Modern experimental design.
Hoboken, N.J. : Wiley-Interscience, ©2007
(DLC) 2006046752
(OCoLC)70668866
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Thomas P Ryan; Wiley InterScience (Online service)
ISBN: 9780470074350 0470074353 9780470074343 0470074345
OCLC Number: 123502068
Description: 1 online resource (xvi, 593 pages) : illustrations
Contents: Modern Experimental Design; Contents; Preface; 1 Introduction; 2 Completely Randomized Design; 3 Designs that Incorporate Extraneous (Blocking) Factors; 4 Full Factorial Designs with Two Levels; 5 Fractional Factorial Designs with Two Levels; 6 Designs With More Than Two Levels; 7 Nested Designs; 8 Robust Designs; 9 Split-Unit, Split-Lot, and Related Designs; 10 Response Surface Designs; 11 Repeated Measures Designs; 12 Multiple Responses; 13 Miscellaneous Design Topics; 14 Tying It All Together; Answers to Selected Exercises; Appendix: Statistical Tables; Author Index; Subject Index.
Series Title: Wiley series in probability and statistics.
Responsibility: Thomas P. Ryan.
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Abstract:

Suitable for use as either an undergraduate or graduate text, depending on the audience, this book covers a wide range of topics, including some that have received increased attention in recent years, such as hard-to-change factors, uniform designs, multiple response optimization, and Analysis of Means (ANOM).

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