Six Sigma Distribution Modeling

Hardcover | January 9, 2007

byAndrew Sleeper

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Sleeper provides six sigma practitioners with the tools which will allow them to stand out from your competitors by using advanced statistical and modeling tools for more in-depth analysis.Understanding and properly utilizing statistical data distributions is one of the most important and difficult skills for a six sigma practitioner to possess. Sleeper provides six sigma practitioners with a road map for selecting and using distributions for more precise outcomes. With the added value of Crystal Ball Modeling software, this book becomes a powerful tool for analyzing and modeling difficult data quickly and efficiently.

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From the Publisher

Sleeper provides six sigma practitioners with the tools which will allow them to stand out from your competitors by using advanced statistical and modeling tools for more in-depth analysis.Understanding and properly utilizing statistical data distributions is one of the most important and difficult skills for a six sigma practitioner t...

From the Jacket

Capitalize on Distribution Models to Achieve Accuracy and Precision in Six Sigma AnalysisThere's no better way for Six Sigma practitioners to improve their statistical skills than with Six Sigma Distribution Modeling. This expert guide shows them how to apply distribution models and statistical modeling tools to analyze systems with a...

Andrew Sleeper is a Master Black Belt and General Manager of Successful Statistics, LLC. Since 1981, he has worked with product development teams as an engineer, statistician, project manager, Six Sigma Black Belt, and consultant. An experienced instructor of statistical tools for engineers, Mr. Sleeper has presented thousands of hours...

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Format:HardcoverDimensions:9 × 6.2 × 1.15 inPublished:January 9, 2007Publisher:McGraw-Hill EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0071482784

ISBN - 13:9780071482783

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Table of Contents

Chapter 1: Modeling Random Behavior with Probability Distributions

Chapter 2: Selecting Statistical Software Tools for Six Sigma Practitioners

Chapter 3: Applying Nonnormal Distribution Models in Six Sigma Projects

Chapter 4: Applying Distribution Models and Simulation in Six Sigma Projects

Chapter 5: Glossary of Terms

Chapter 6: Bernouli (Yes-No) Distribution Family

Chapter 7: Beta Distribution Family

Chapter 8: Binomial Distribution Family

Chapter 9: Chi-Squared Distribution Family

Chapter 10: Discrete Uniform Distribution Family

Chapter 11: Exponential Distribution Family

Chapter 12: Extreme Value (Gumbel) Distribution Family

Chapter 13: F Distribution Family

Chapter 14: Gamma Distribution Family

Chapter 15: Geometric Distribution Family

Chapter 16: Hypergeometric Distribution Family

Chapter 17: Laplace Distribution Family

Chapter 18: Logistic Distribution Family

Chapter 19: Logonormal Distribution Family

Chapter 20: Negative Binomial Distribution Family

Chapter 21: Normal (Gaussian) Distribution Family

Chapter 22: Pareto Distribution Family

Chapter 23: Poisson Distribution Family

Chapter 24: Rayleigh Distribution Family

Chapter 25: Student's Distribution Family

Chapter 26: Triangular Distribution Family

Chapter 27: Uniform Distribution Family

Chapter 28: Weibull Distribution Family

REFERENCES

INDEX