Optimum experimental designs, with SAS
Oxford University Press, 2007 - Computers - 511 pages
Experiments in the field and in the laboratory cannot avoid random error,and statistical methods are essential for their efficient design and analysis.This book focuses on the use of SAS, a powerful software package that provides acomplete set of statistical tools including analysis of variance, regression,categorical data analysis, and multivariate analysis. Many examples of SAS code,results, plots and tables are provided, along with a fully supported website.The text contains numerous figures, and end of chapter notes on further reading.Exercises consolidating the material covered are given in the final chapter.Authored by leading experts, this book is ideal for statisticians in academia,research and the medical, pharmaceutical and chemical industries.
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32 factorial algorithm analysis approximation Atkinson Bayesian blocking variables candidate points carbon monoxide central composite designs centre points Chapter compound design concentration constraints construction covariance criteria D-efficiency D-optimality data set defined depend design criterion design matrix design of experiments design of Table design points design region design weights discussed Donev effects efficiency elastomer equivalence theorem error exact designs Example exponential decay Figure first-order model grid information matrix interactions least squares levels linear models locally D-optimum design locally optimum maximum value measure methods minimize mixture experiments model checking non-linear models number of trials observations optimization optimum design orthogonal parameter estimates parameter values plot points of support polynomial prior distribution prior information PROC OPTEX procedure quadratic model qualitative factor quantitative random replicated residuals SAS Task second-order model second-order response surface sequential structure support points theophylline transformation treatment vector viscosity yield zero