Multivariate Statistical Methods in Quality Management

Kai Yang

ISBN 10: 0071432086 / ISBN 13: 9780071432085

Published by Mcgraw Hill LLC Mär 2004, 2004

About this Item

Neuware - Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.UPGRADE MANUFACTURING AND SERVICE PERFORMANCE WITH POWERFUL STATISTICAL TOOLS.

Seller Inventory # 9780071432085

Bibliographic Details

• Title: Multivariate Statistical Methods in Quality Management
• Publisher: Mcgraw Hill LLC Mär 2004
• Publication Date: 2004
• Language: English
• ISBN 10: 0071432086
• ISBN 13: 9780071432085
• Binding: Buch
• Condition: Neu
• Dimensions: 234W x 162H x 27D millimeters 234 millimeters width by 162 millimeters height by 27 millimeters depth
• Weight: 576 grams

About this title

Synopsis

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality,  authenticity, or access to any online entitlements included with the product.




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

About the Authors

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.

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