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Six Sigma Distribution Modeling
This resource teaches you how to select and utilize advanced statistical data and modeling tools to achieve the most in-depth analysis possible. You'll be able to convert existing models into dynamic simulation tools, evaluate multiple strategies and outcomes in one simple process, determine and reduce risks in the planning stage, and graphically communicate
information to your clients.
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
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