Multivariate Statistical Methods in Quality Management
by: Kai Yang, Jayant Trewn
Abstract: Multivariate statistical methods are an essential component of quality engineering data analysis. This monograph provides a solid background in multivariate statistical fundamentals and details key multivariate statistical methods, including simple multivariate data graphical display and multivariate data stratification. Graphical multivariate data display, Multivariate regression and path analysis, Multivariate process control charts, Six sigma and multivariate statistical methods.
Full details
Table of Contents
- A. Preface
- 1. Multivariate Statistical Methods and Quality
- 2. Graphical Multivariate Data Display and Data Stratification
- 3. Introduction to Multivariate Random Variables, Normal Distribution, and Sampling Properties
- 4. Multivariate Analysis of Variance
- 5. Principal Component Analysis and Factor Analysis
- 6. Discriminant Analysis
- 7. Cluster Analysis
- 8. Mahalanobis Distance and Taguchi Method
- 9. Path Analysis and the Structural Model
- A. Multivariate Statistical Process Control
- A. Probability Distribution Tables
- B. References
- C. ABOUT THE AUTHORS
Tools & Media
Expanded Table of Contents
- A. Preface
- 1. Multivariate Statistical Methods and Quality
- 2. Graphical Multivariate Data Display and Data Stratification
- 3. Introduction to Multivariate Random Variables, Normal Distribution, and Sampling Properties
- 4. Multivariate Analysis of Variance
- 5. Principal Component Analysis and Factor Analysis
- 6. Discriminant Analysis
- Introduction
- Linear Discriminant Analysis for Two Normal Populations with Known Covariance Matrix
- Linear Discriminant Analysis for Two Normal Populations with Equal Covariance Matrices
- Discriminant Analysis for Two Normal Population with Unequal Covariance Matrices
- Discriminant Analysis for Several Normal Populations
- Case Study: Discriminant Analysis of Vegetable Oil by Near-Infrared Reflectance Spectroscopy
- 7. Cluster Analysis
- 8. Mahalanobis Distance and Taguchi Method
- 9. Path Analysis and the Structural Model
- A. Multivariate Statistical Process Control
- A. Probability Distribution Tables
- B. References
- C. ABOUT THE AUTHORS
Book Details
Title: Multivariate Statistical Methods in Quality Management
Publisher: McGraw-Hill: New York, Chicago, San Francisco, Lisbon, London, Madrid, Mexico City, Milan, New Delhi, San Juan, Seoul, Singapore, Sydney, Toronto
Copyright / Pub. Date: 2004 The McGraw-Hill Companies, Inc.
ISBN: 9780071432085
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 is the author of this McGraw-Hill Professional publication.
Description: Multivariate statistical methods are an essential component of quality engineering data analysis. This monograph provides a solid background in multivariate statistical fundamentals and details key multivariate statistical methods, including simple multivariate data graphical display and multivariate data stratification. Graphical multivariate data display, Multivariate regression and path analysis, Multivariate process control charts, Six sigma and multivariate statistical methods.
