Multivariate Statistical Methods in Quality Management

UPGRADE MANUFACTURING AND SERVICE PERFORMANCE WITH POWERFUL STATISTICAL TOOLS

There's no better way to master the most rigorous statistical methods available for analyzing the performance of complex systems — Multivariate Statistical Methods in Quality Management teaches powerful analytic tools for troubleshooting, root cause analysis, process control, quality improvement, and many other applications. Written by statistics experts who specialize in reliability and quality engineering, this unique resource introduces the fundamentals and then demonstrates how to:

*Choose the best method for each data set

* Make complex data intelligible with graphical tools

* Coax important hidden information from data with graphical 3-D software and numerical multivariate data stratification

* Perform data reduction with principal component analysis, factor analysis, and discriminant analysis

* Apply multivariate methods in Six Sigma enterprises

* Get clarification from case studies, models, and 50 illustrations

* Uncover the source of problems and pinpoint solutions in arenas from manufacturing processes to sales performance and beyond

Kai Yang, Ph.D., has consulted extensively in many areas of quality and reliability engineering. He is Associate Professor of Industrial and Manufacturing Engineering at Wayne State University, Detroit, Michigan. He lives in West Bloomfield, Michigan.

Jayant Trewn, Ph.D., is a research faculty member at Beaumont Hospital in Royal Oak, Michigan. He is responsible for implementing cutting edge industrial engineering tools in hospital and health care management. Dr. Trewn was a Director of Quality and Productivity Improvement at Vetri Systems, a Lason Company. He was responsible for business process design and improvement in the global business environment.

Yang (industrial and manufacturing engineering, Wayne State University) and Trewn (research faculty, Beaumont Hospital) explain analytical tools for trouble-shooting, root cause analysis, process control, quality improvement, and other applications in business and industry. Writing for quality professionals, they discuss the theory and background of each method and give examples illustrating how these multivariate statistical methods can be used to solve real world problems, then show how to integrate multivariate statistical methods in quality assurance practice and Six Sigma projects. Readers should have some background in univariate statistical concepts and simple data analysis techniques, as well as in matrix algebra.

Kai Yang, Ph.D., has consulted extensively in many areas of quality and reliability engineering. He is Associate Professor of Industrial and Manufacturing Engineering at Wayne State University, Detroit, Michigan. He lives in West Bloomfield, Michigan.

Jayant Trewn, Ph.D., is a research faculty member at Beaumont Hospital in Royal Oak, Michigan. He is responsible for implementing cutting edge industrial engineering tools in hospital and health care management. Dr. Trewn was a Director of Quality and Productivity Improvement at Vetri Systems, a Lason Company. He was responsible for business process design and improvement in the global business environment.