David Ratkowsky, Richard Alldredge, Marc A. Evans

Cross-Over Experiments (eBook, PDF)

Design, Analysis and Application

• Format: PDF

Produktbeschreibung

Developing a model-based approach that enables any cross-over trial, of any degree of imbalance, to be analyzed both for direct effects and for residual effects using consistent procedures that employ commercially available statistical software, this timely reference/ text offers a thorough guide to the analysis of cross-over designs.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 480
• Erscheinungstermin: 29. Januar 2020
• Englisch
• ISBN-13: 9781000715705
• Artikelnr.: 58595706

Autorenporträt

DAVID A. RATKOWSKY is a former Assistant Professor of Statistics at Washington State University, Pullman. A member of the Biometric Society, he is the author of the Handbook of Nonlinear Regression Models and Nonlinear Regression Modeling: A Unified Practical Approach (both titles, Marcel Dekker, Inc.) and the author or co-author of more than 80 professional papers. He received the B.Ch.E. degree (1956) from City College, New York, New York, and the Ph.D. degree (1960) in chemical engineering from the University of Washington, Seattle.

MARC A. EVANS is an Assistant Professor of Statistics at Washington State University, Pullman. A member of the Biometric Society, he received the B.A. degree (1984) in mathematics from Humboldt State University, Arcata, California, and the M.S. (1987) and Ph.D. (1989) degrees in statistics from the University of Wyoming, Laramie.

J. RICHARD ALLDREDGE is Director of the Program in Statistics at Washington State University, Pullman. He is a member of the American Statistical Association, the Biometric Society, the Institute of Mathematical Statistics, and the International Institute for Mathematical Geology. Dr. Alldredge received the B.A. degree (1969) in mathematics from Oakland University, Rochester, Michigan, the M.S. degree (1971) in statistics from Colorado State University, Fort Collins, and the Ph.D. degree (1980) in statistics from Texas A & M University, College Station.