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Statistical Methods for Six Sigma - in R&D and Manufacturing
Author: Anand Joglekar       Publisher: John Wiley & Sons, Inc.

Statistical Methods for Six SigmaWritten by a recognized and well-established educator in the field, Statistical Methods for Six Sigma – in R&D and Manufacturing is specifically meant for engineers, scientists, technical managers and other technical professionals in industry. Featuring an emphasis on practical learning, applications and 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 contribution 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 and covers a large number of useful statistical methods compactly, in a language and depth necessary to make successful applications. The book also contains a wealth of case studies and examples, and features a unique test called “what color is your belt?” to evaluate the reader’s understanding of the subject.

Recent Industry Presentations

These are some seminars we recently delivered to a variety of industrial audiences. Each seminar is one to two hours long. The seminars were delivered for various R&D and manufacturing groups, often with examples customized to the audience.

  1. Does my project really need six-sigma quality?
  2. The role of statistical methods in six sigma
  3. How much data to collect? — attribute data
  4. How much data to collect? — variable data
  5. Confidence intervals vs. hypothesis testing
  6. Why design experiments?
  7. Projective properties of screening designs
  8. Multi-level designs using Taguchi linear graphs
  9. Mixture designs
  10. Mixture or ratios?
  11. The principle of robust design and consequences for R&D
  12. The importance of data transformation
  13. Normality and data transformation
  14. Analysis of validation and pre-validation experiments
  15. Size matters — how good is your Cpk, really?
  16. How stable and capable are our production processes?
  17. The statistics of limits— spec, control, action, release...
  18. Regression — simple and weighted
  19. Is my R-square too low?
  20. Understanding variance components
  21. Making decisions using variance components
  22. Variance transmission, robustness and specifications
  23. Specifications and implied constraints on Sw and Sb
  24. Acceptance criteria for measurement systems
  25. Accelerated stability tests
  26. The statistics of method transfer
  27. Why does random failure imply exponential time to failure
  28. Does acceptance sampling buy us what we think?
  29. The theory of significant figures
  30. Understanding key software outputs


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