Experiments: Planning, Analysis and Parameter Design Optimization (Wiley Series in Probability and Statistics) - Tapa dura

Acerca del autor

C. F. JEFF WU, PhD, is H. C. Carver Professor, Department of Statistics and Industrial and Operations Engineering, University of Michigan, Ann Arbor. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and a recipient of the COPSS Award and numerous other awards and prizes. He is the author of about 100 published papers. MICHAEL HAMADA, PhD, is a technical staff member in the Statistical Sciences Group at Los Alamos National Laboratory in New Mexico. He has published 30 papers and has won the Technometrics' Wilcoxon Prize and the ASQC Brumbaugh Award.

De la contraportada

A modern and highly innovative guide to industrial experimental design

The past two decades have seen major progress in the use of statistically designed experiments for product and process improvement. In this new work, Jeff Wu and Michael Hamada, two highly recognized researchers in the field, introduce some of the newest discoveries in the design and analysis of experiments as well as their applications to system optimization, robustness, and treatment comparisons in the diverse fields of engineering, technology, agriculture, biology, and medicine.

Drawing on examples from their impressive roster of industrial clients (including GM, Ford, AT&T, Lucent Technologies, and Chrysler), Wu and Hamada modernize accepted methodologies, while presenting many cutting-edge topics for the first time in a single, easily accessible source. These include robust parameter design, reliability improvement, analysis of nonnormal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Other features include:

• Coverage of parameter design for system improvement first introduced by Taguchi in the mid-1980s
• An innovative approach to the treatment of design tables
• A discussion of new computing techniques, including graphical methods, generalized linear models, and Bayesian computing via Gibbs samplers
• Each chapter motivated by a real experiment
• Extensive case studies, including goals, data, and experimental plans
• More than 80 data sets as well as hundreds of charts, tables, and figures