Anthony Atkinson – författare

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.

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.

This book is about using graphs to understand the relationship between a regression model and the data to which it is fitted. Because of the new way in which models are fitted, for example by least squares, we can lose information about the effect of individual observations on inferences about the form and parameters of the model. The methods developed in this book reveal how the fitted regression model depends on individual observations and on groups of observations. Robust procedures can sometimes reveal this structure, but downweight or discard some observations. The novelty in this book is to combine robustness and a "forward" search through the data with regression diagnostics and computer graphics.

This volume presents the state of optimum experimental design at the beginning of the new millennium, with an emphasis on developing areas. The contributions range from theory to applications, starting with a glimpse back to the beginnings of optimum experimental design. Theoretical chapters cover the properties and methods of construction of designs. Applications include chapters on sequential design problems in the pharmaceutical industry and on the designs with discrete factors in agriculture. There are chapters on training neural networks, on the efficient selection of sampling methods, and on problems arising in glass making and in herbicide resistance of Brazilian weeds. The contributors, from a variety of countries, include many acknowledged experts whose work reflects the international spread of activity in the subject. Experimentalists as well as research workers and students in statistics should find much to interest them in these papers.

Suitable for upper level advanced management or cost accounting courses at the undergraduate or MBA/graduate level. Assumes knowledge of management and/or cost accounting.

This text provides leading-edge treatment of innovative management accounting issues used by major companies throughout the world. Takes a systematic management- oriented approach to advanced management topics. Each chapter is accompanied by cases to illustrate the concepts discussed.

This volume contains many of the papers presented at the conference "Optimum Design 2000: Prospects for the New Millennium" held in Cardiff, UK on April 12th - 14th, 2000. The majority of the papers consider aspects of optimum experimental design from the theoretical to applications. Many of the optimisation problems arising in the optimum design theory in general and the articles in this volume in particular, fall into the category of nonconvex, including global, optimization. The papers are organised in two sections. Since we are at the beginning of a new millennium the first paper starts by looking back at the beginnings of optimum exper­ imental design in 1917 and sketches the development up to Kiefer's paper read to the Royal Statistical Society in 1959. This is the first in a group of papers which we have labelled "Theory", which cover the more general aspects, such as the properties and methods of construction of designs. In the "Applications" section there are papers on sequential design problems arising in the pharmaceutical industry and on the designs with discrete factors which occur in agriculture. There are also papers on training neural networks, on the efficient design of sampling methods, on design problems in mixtures, arising from a study of glass manufacturing, and on the resistance of Brazilian weeds to herbicides. The number attending the meeting was deliberately limited to 50 in order to encour­ age a workshop atmosphere and the exchange of ideas.

This book is about using graphs to understand the relationship between a regression model and the data to which it is fitted. The methods developed in this book reveal how the fitted regression model depends on individual observations and on groups of observations.