Six Sigma and Beyond: Statistics and Probability, Volume III

  • 5h 52m
  • D. H. Stamatis
  • CRC Press
  • 2003

Researchers and professionals in all walks of life need to use the many tools offered by the statistical world, but often do not have the necessary experience in both concept and application. No matter what your profession, sooner or later numbers need to be crunched, and often you need to understand how to do it, and why it is important. Quality control is no different. Six Sigma and Beyond: Statistics and Probability covers the concepts of some useful statistical tools, appropriate formulae for specific tools, the connection of statistics to probability, and how to use them.

This volume introduces the relationship of statistics, probability, and reliability as they apply to quality in general and to Six Sigma in particular. The author brings the theoretical into the practical by providing statistical techniques, tests, and methods that the reader can use in any organization. He reviews basic parametric and non-parametric statistics, probability concepts and applications, and addresses topics for both measurable and attribute characteristics. He delineates the importance of collecting, analyzing, and interpreting data not from an academic point of view but from a practical perspective.

This is not a textbook but a guide for anyone interested in statistical, probability, and reliability to improve processes and profitability in their organizations. When you begin a study of something, you want to do it well. You want to design a good study, analyze the results properly, and prepare a cogent report that summarizes what you've found. Six Sigma and Beyond: Statistics and Probability shows you how to use statistical tools to improve your processes and give your organization the competitive edge.

About the Author

D. H. Stamatis, Ph.D., ASQC-Fellow, CQE, CMfgE, is currently president of Contemporary Consultants, in Southgate, Michigan. He received his B.S. and B.A. degrees in marketing from Wayne State University, his Master's degree from Central Michigan University, and his Ph.D. degree in instructional technology and business/statistics from Wayne State University.

Dr. Stamatis is a certified quality engineer for the American Society of Quality Control, a certified manufacturing engineer for the Society of Manufacturing Engineers, and a graduate of BSIIs ISO 9000 lead assessor training program.

He is a specialist in management consulting, organizational development, and quality science and has taught these subjects at Central Michigan University, the University of Michigan, and Florida Institute of Technology.

With more than 30 years of experience in management, quality training, and consulting, Dr. Stamatis has served and consulted for numerous industries in the private and public sectors. His consulting extends across the United States, Southeast Asia, Japan, China, India, and Europe. Dr. Stamatis has written more than 60 articles and presented many speeches at national and international conferences on quality. He is a contributing author in several books and the sole author of 12 books. In addition, he has performed more than 100 automotive-related audits and 25 preassessment ISO 9000 audits, and has helped several companies attain certification. He is an active member of the Detroit Engineering Society, the American Society for Training and Development, the American Marketing Association, and the American Research Association, and a fellow of the American Society for Quality Control.

In this Book

  • Designing and using Forms for Studies
  • Counting Frequencies
  • Summarizing Data
  • Working with the Normal Distribution
  • Testing Hypotheses About Two Independent Means
  • Testing Hypotheses About Two Dependent Means
  • Comparing Several Means
  • Measuring Association
  • Calculating Regression Lines
  • Common Miscellaneous Statistical Tests
  • Advanced Topics in Statistics
  • Time Series and Forecasting
  • Functions of Real and Random Variables
  • Set Theory
  • Permutations and Combinations
  • Discrete and Continuous Random Variables