Design For Six SigmaTolerance Design, and DOE to Optimize Product Quality and ReliabilityDesign For Six Sigma
Design For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and ReliabilityDesign For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and ReliabilityDesign For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and ReliabilityDesign For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and ReliabilityDesign For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and ReliabilityDesign For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and ReliabilityDesign For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and Reliability

       
   
       

Six Sigma Distribution Modeling

by Andy Sleeper
(c) 2007 McGraw-Hill

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Design For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and Reliability
Design For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and Reliability

Design For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and Reliability
Design For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and Reliability
   

Six Sigma Distribution Modeling

Distribution models are essential tools for

  • Estimating process characteristics

  • Predicting future values

  • Simulating process behavior

  • Communicating random concepts

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Six Sigma Distribution Modeling is the first book providing expert guidance on selecting distribution models, plus a comprehensive catalog of distribution families. Written for non-statisticians, this landmark reference makes it easy to choose a properly fitting distribution model and use it for a variety of important tasks.

Six Sigma training and many statistical tools assume that processes have a normal distribution. When they do not, statistical tools must be changed to fit the process. Six Sigma Distribution Modeling illustrates the best ways to create control charts, calculate capability metrics, and predict process performance using a wide variety of distribution models.

No other book provides expert guidance on all these important tools:

  • Goodness-of-fit tests

  • Graphs for selecting distributions

  •  Estimating population characteristics

  •  Nonnormal capability metrics

  •  Nonnormal control charts

  •  Optimizing random processes

  •  Extreme value theory

  • Selecting and using statistical software

Table of Contents:

  1. Modeling random behavior with probability distributions

  2. Selecting statistical software tools for Six Sigma practitioners

  3. Applying nonnormal distribution models in Six Sigma projects

  4. Applying distribution models and simulation in Six Sigma projects

  5. Glossary of Terms

  6. Bernoulli (Yes-No) distribution

  7. Beta distribution

  8. Binomial distribution

  9. Chi-squared distribution, including chi and noncentral versions

  10. Discrete uniform distribution

  11. Exponential distribution

  12. Extreme value distribution

  13. F distribution, including noncentral version

  14. Gamma distribution

  15. Geometric distribution

  16. Hypergeometric distribution

  17. Laplace distribution

  18. Logistic distribution

  19. Lognormal distribution

  20. Negative binomial distribution

  21. Normal (Gaussian) distribution, including half-normal and truncated versions

  22. Pareto distribution

  23. Poisson distribution, including truncated versions

  24. Rayleigh distribution

  25. Studentís t distribution, including noncentral version

  26. Triangular distribution

  27. Uniform distribution

  28. Weibull distribution

   
Design For Six Sigma, Tolerance Design, and DOE to Optimize Product Quality and Reliability