633 pages, Illus
Rewritten and updated, this new edition of "Statistics for Experimenters" adopts the same approaches as the landmark First Edition by teaching with examples, readily understood graphics, and the appropriate use of computers. Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors' practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.
Among the new topics included are: Graphical Analysis of Variance; Computer Analysis of Complex Designs; Simplification by transformation; Hands-on experimentation using Response Service Methods; Further development of robust product and process design using split plot arrangements and minimization of error transmission; Introduction to Process Control, Forecasting and Time Series; Illustrations demonstrating how multi-response problems can be solved using the concepts of active and inert factor spaces and canonical spaces; and Bayesian approaches to model selection and sequential experimentation. An appendix featuring Quaquaversal quotes from a variety of sources including noted statisticians and scientists to famous philosophers is provided to illustrate key concepts and enliven the learning process. All the computations in the Second Edition can be done utilizing the statistical language R. Functions for displaying ANOVA and lamba plots, Bayesian screening, and model building are all included and R packages are available online.
Complete with applications covering the physical, engineering, biological, and social sciences, "Statistics for Experimenters" is designed for individuals who must use statistical approaches to conduct an experiment, but do not necessarily have formal training in statistics. Experimenters need only a basic understanding of mathematics to master all the statistical methods presented. This text is an essential reference for all researchers and is a highly recommended course book for undergraduate and graduate students.
Science and Statistics. COMPARING TWO TREATMENTS. Use of External Reference Distribution to Compare Two Means. Random Sampling and the Declaration of Independence. Randomization and Blocking with Paired Comparisons. Significance Tests and Confidence Intervals for Means, Variances, Proportions and Frequences. COMPARING MORE THAN TWO TREATMENTS. Experiments to Compare k Treatment Means. Randomized Block and Two--Way Factorial Designs. Designs with More Than One Blocking Variable. MEASURING THE EFFECTS OF VARIABLES. Empirical Modeling. Factorial Designs at Two Levels. More Applications of Factorial Designs. Fractional Factorial Designs at Two Levels. More Applications of Fractional Factorial Designs. BUILDING MODELS AND USING THEM. Simple Modeling with Least Squares (Regression Analysis). Response Surface Methods. Mechanistic Model Building. Study of Variation. Modeling Dependence: Times Series. Appendix Tables. Index.
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George E.P. Box is R.A. Fisher Professor of Statistics at the University of Wisconsin. He received the degrees of Ph.D. and D.Sc. in mathematical statistics from the University of London and an Honorary D.Sc. from the University of Rochester. He had many years of experience as a practicing statistician in the British Army and later in Imperial Chemical Industries, Ltd., and has served as a consultant to industry and government. He is a Fellow of the American Academy of Arts and Sciences, and a recipient of the Wilks memorial medal of the American Statistical Association, the Shewhart medal of the American Society for Quality Control, and the Guy medal in silver of the Royal Statistical Society. He is an author of over 100 published papers and of the following books: Time Series Analysis Forecasting and Control (with G. M. Jenkins); Bayesian Inference in Statistical Analysis (with G. C. Tiao); and Evolutionary Operation (with N. R. Draper). William G. Hunter is Professor of Statistics and Engineering at the University of Wisconsin, Madison. In chemical engineering he received degrees of B.S.E. from Princeton University and M.S. E. from the University of Illinois. In statistics he received M.S. and Ph.D. degrees from the University of Wisconsin. He has also taught at the University of Ife in Nigeria, the University of Singapore, and Imperial College of Science and Technology in England. Dr. Hunter is an author of more than fifty technical articles and is an Associate Editor of Technometrics. A consultant, he ha also rpesented over sixty short courses for industry, government, and such organizations as the American Association for the Advancement of Science, the American Institute of Chemical Engineers, the American Society for Quality Control, and the United Nations. J. Stuart Hunter is Professor of Civil Engineering at Princeton University. He received his B.S. in electrical engineering, his M.S. in engineering mathematics from North Carolina State University, and his Ph.D. in experimental statistics from the Institute of Statistics at North Carolina State University and the University of North Carolina. A leader in the exposition of statistical methods for over 25 years, Dr. Hunter has served as consultant to many industries and government agencies. He has been a staff member of the National Academy of Science, Committee on National Statistics, and Statistician in Residence at the University of Wisconsin, and is the Founding Editor of Technometrics. He is a member of numerous professional societies, has published extensively, and is the coauthor (with I. Guttman and S.S. Wilks) of Introductory Engineering Statistics, Second Edition (published by Wiley--Interscience).