Statistical Methods for Six Sigma: In R&D and Manufacturing

✍ Scribed by Anand M. Joglekar(auth.)

• Year: 2003
• Tongue: English
• Leaves: 326
• Category: Library

✦ Synopsis

A guide to achieving business successes through statistical methods

Statistical methods are a key ingredient in providing data-based guidance to research and development as well as to manufacturing. Understanding the concepts and specific steps involved in each statistical method is critical for achieving consistent and on-target performance.

Written by a recognized educator in the field, Statistical Methods for Six Sigma: In R&D and Manufacturing is specifically geared to engineers, scientists, technical managers, and other technical professionals in industry. Emphasizing practical learning, applications, and performance improvement, Dr. Joglekar s text shows today s industry professionals how to:

• Summarize and interpret data to make decisions
• Determine the amount of data to collect
• Compare product and process designs
• Build equations relating inputs and outputs
• Establish specifications and validate processes
• Reduce risk and cost-of-process control
• Quantify and reduce economic loss due to variability
• Estimate process capability and plan process improvements
• Identify key causes and their contributions to variability
• Analyze and improve measurement systems

This long-awaited guide for students and professionals in research, development, quality, and manufacturing does not presume any prior knowledge of statistics. It covers a large number of useful statistical methods compactly, in a language and depth necessary to make successful applications. Statistical methods in this book include: variance components analysis, variance transmission analysis, risk-based control charts, capability and performance indices, quality planning, regression analysis, comparative experiments, descriptive statistics, sample size determination, confidence intervals, tolerance intervals, and measurement systems analysis. The book also contains a wealth of case studies and examples, and features a unique test to evaluate the reader s understanding of the subject.Content:
Chapter 1 Introduction (pages 1–12):
Chapter 2 Basic Statistics (pages 13–47):
Chapter 3 Comparative Experiments and Regression Analysis (pages 49–93):
Chapter 4 Control Charts (pages 95–133):
Chapter 5 Process Capability (pages 135–152):
Chapter 6 Other Useful Charts (pages 153–175):
Chapter 7 Variance Components Analysis (pages 177–200):
Chapter 8 Quality Planning with Variance Components (pages 201–240):
Chapter 9 Measurement Systems Analysis (pages 241–275):
Chapter 10 What Color Is Your Belt? (pages 277–301):
Chapter 04 Appendix D1: k Values for Two?Sided Normal Tolerance Limits (page 306):