Optimum Experimental Designs, With SAS by Anthony Atkinson, Alexander Donev, and Randall Tobias (2007) is a comprehensive guide to experimental design and analysis using SAS. The book is aimed at students and researchers in statistics, as well as professionals in the pharmaceutical, medical, and chemical industries. It provides an introduction to the principles of experimental design, including the importance of models in data analysis, least squares fitting, and simple optimum designs. The second part of the book offers a detailed discussion of the general theory of optimum design and a wide variety of experimental designs, emphasizing the use of SAS for practical solutions in both standard and non-standard situations. The book is written for readers with little prior statistical knowledge, and it includes numerous figures, end-of-chapter notes on further reading, and a supporting website with SAS program codes, problems, and solutions. The authors are leading experts in their fields, and the book is ideal for students and researchers in academia, research, and the process and pharmaceutical industries. The book covers topics such as D-optimum designs, algorithms for constructing exact D-optimum designs, experiments with both qualitative and quantitative factors, blocking response surface designs, mixture experiments, nonlinear models, Bayesian optimum designs, design augmentation, model checking, compound design criteria, generalized linear models, response transformation, and time-dependent models with correlated observations. The book is structured into two parts: Background and Theory and Applications. The first part includes chapters on introduction, key ideas, experimental strategies, model selection, least squares, criteria for good experiments, standard designs, and the analysis of experiments. The second part includes chapters on optimum design theory, optimality criteria, D-optimum designs, algorithms for constructing exact D-optimum designs, experiments with both qualitative and quantitative factors, blocking response surface designs, mixture experiments, nonlinear models, Bayesian optimum designs, design augmentation, model checking, compound design criteria, generalized linear models, response transformation, and time-dependent models with correlated observations. The book also includes a bibliography, author index, and subject index. The book is published by Oxford University Press and is available in hardback format with a price of £75.00. The ISBN is 978-0-19-929659-0. The book is part of the Oxford Statistical Science series, series number 34. The book is available online through the Oxford University Press website and is also available in the University of Manchester's MIMS EPrint collection. The book is intended for a wide audience, including students and researchers in statistics, as well as professionals in the pharmaceutical, medical, and chemical industries. The book is written in a clear and accessible style, with a focus on practical applications and the use of SAS for experimental design and analysis. The book is an essential resource for anyone involved in experimental design and analysis, and it provides a comprehensive overview of the subject.Optimum Experimental Designs, With SAS by Anthony Atkinson, Alexander Donev, and Randall Tobias (2007) is a comprehensive guide to experimental design and analysis using SAS. The book is aimed at students and researchers in statistics, as well as professionals in the pharmaceutical, medical, and chemical industries. It provides an introduction to the principles of experimental design, including the importance of models in data analysis, least squares fitting, and simple optimum designs. The second part of the book offers a detailed discussion of the general theory of optimum design and a wide variety of experimental designs, emphasizing the use of SAS for practical solutions in both standard and non-standard situations. The book is written for readers with little prior statistical knowledge, and it includes numerous figures, end-of-chapter notes on further reading, and a supporting website with SAS program codes, problems, and solutions. The authors are leading experts in their fields, and the book is ideal for students and researchers in academia, research, and the process and pharmaceutical industries. The book covers topics such as D-optimum designs, algorithms for constructing exact D-optimum designs, experiments with both qualitative and quantitative factors, blocking response surface designs, mixture experiments, nonlinear models, Bayesian optimum designs, design augmentation, model checking, compound design criteria, generalized linear models, response transformation, and time-dependent models with correlated observations. The book is structured into two parts: Background and Theory and Applications. The first part includes chapters on introduction, key ideas, experimental strategies, model selection, least squares, criteria for good experiments, standard designs, and the analysis of experiments. The second part includes chapters on optimum design theory, optimality criteria, D-optimum designs, algorithms for constructing exact D-optimum designs, experiments with both qualitative and quantitative factors, blocking response surface designs, mixture experiments, nonlinear models, Bayesian optimum designs, design augmentation, model checking, compound design criteria, generalized linear models, response transformation, and time-dependent models with correlated observations. The book also includes a bibliography, author index, and subject index. The book is published by Oxford University Press and is available in hardback format with a price of £75.00. The ISBN is 978-0-19-929659-0. The book is part of the Oxford Statistical Science series, series number 34. The book is available online through the Oxford University Press website and is also available in the University of Manchester's MIMS EPrint collection. The book is intended for a wide audience, including students and researchers in statistics, as well as professionals in the pharmaceutical, medical, and chemical industries. The book is written in a clear and accessible style, with a focus on practical applications and the use of SAS for experimental design and analysis. The book is an essential resource for anyone involved in experimental design and analysis, and it provides a comprehensive overview of the subject.