Optimum Experimental Designs, With Sas

Optimum Experimental Designs, With Sas

AngličtinaMěkká vazbaTisk na objednávku
Atkinson Anthony
Oxford University Press
EAN: 9780199296606
Tisk na objednávku
Předpokládané dodání v pátek, 7. června 2024
1 898 Kč
Běžná cena: 2 109 Kč
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Podrobné informace

Experiments on patients, processes or plants all have random error, making statistical methods essential for their efficient design and analysis. This book presents the theory and methods of optimum experimental design, making them available through the use of SAS programs. Little previous statistical knowledge is assumed. The first part of the book stresses the importance of models in the analysis of data and introduces least squares fitting and simple optimum experimental designs. The second part presents a more detailed discussion of the general theory and of a wide variety of experiments. The book stresses the use of SAS to provide hands-on solutions for the construction of designs in both standard and non-standard situations. The mathematical theory of the designs is developed in parallel with their construction in SAS, so providing motivation for the development of the subject. Many chapters cover self-contained topics drawn from science, engineering and pharmaceutical investigations, such as response surface designs, blocking of experiments, designs for mixture experiments and for nonlinear and generalized linear models. Understanding is aided by the provision of "SAS tasks" after most chapters as well as by more traditional exercises and a fully supported website. The authors are leading experts in key fields and this book is ideal for statisticians and scientists in academia, research and the process and pharmaceutical industries.
EAN 9780199296606
ISBN 019929660X
Typ produktu Měkká vazba
Vydavatel Oxford University Press
Datum vydání 24. května 2007
Stránky 528
Jazyk English
Rozměry 240 x 168 x 29
Země United Kingdom
Autoři Atkinson Anthony; Donev Alexander; Tobias Randall
Ilustrace numerous line illustrationss
Série Oxford Statistical Science Series