Six Sigma Distribution Modeling

Overview
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.

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

Biographical note
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 of training in countries around the world. Mr. Sleeper is also the author of Design For Six Sigma Statistics: 59 Tools for Diagnosing and Solving Problems in DFSS Initiatives, published by McGraw-Hill.

Back cover copy
Capitalize on Distribution Models to Achieve Accuracy and Precision in Six Sigma Analysis

There'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 accuracy and precision¿and stand out from their competitors.

With the added value of Crystal Ball simulation software, this unique reference enables users to quickly select models to represent random processes…develop dynamic simulation tools…evaluate multiple strategies and outcomes in one easy procedure…and understand which inputs control the variability of their forecasts.

Six Sigma Distribution Modeling also helps technical professionals to identify and reduce their risks in the planning stage of projects, prior to costly implementation, and to graphically communicate statistical information to clients, managers, and peers. This vital statistical modeling reference features:

Clear, concise guidance on selecting distribution models
Goodness-of-fit testing
Step-by-step calculation methods
Detailed explanations of numerous distribution families
Free 140-day trial of Crystal Ball software

Filled with over 120 helpful illustrations, Six Sigma Distribution Modeling now equips technical professionals with a full array of advanced tools for modeling, simulating, and optimizing any system in a Six Sigma project.

Six Sigma Distribution Modeling now equips Six Sigma professionals with a detailed road map for selecting and implementing distribution models for more accurate outcome projections. With the added value of Crystal Ball simulation software, this skills-building book is a powerful resource for analyzing and modeling complex systems quickly and easily.

Six Sigma Distribution Modeling includes a wealth of guidance on distribution model selection…goodness-of-fit testing…step-by-step calculation methods…and detailed explanations of numerous distribution families. This landmark reference offers expert coverage of:

Selecting Distribution Models
Applying Nonnormal Distributions in Six Sigma Environments
Case Studies of Modeling and Simulation
Terminology for Describing Distribution Models
Bernoulli Distribution
Beta Distribution
Binomial Distribution
Chi-Squared Distribution -- including chi and noncentral versions
Discrete Uniform Distribution
Exponential Distribution
Extreme Value (Gumbel) Distribution
F Distribution -- including noncentral F
Gamma Distribution -- including Erlang
Geometric Distribution
Hypergeometric Distribution
Laplace Distribution
Logistic Distribution -- including loglogistic
Lognormal Distribution
Negative Binomial Distribution -- including Pascal
Normal Distribution -- including half-normal and truncated normal
Poisson Distribution
Rayleigh Distribution
T Distribution -- including noncentral T
Triangular Distribution
Uniform Distribution
Weibull Distribution
References

With the state-of-the-art guidance in this focused guide, Six Sigma professionals will be able to quickly convert their existing models into dynamic simulation tools, as well as evaluate multiple strategies and outcomes in one easy process. The book will also help them understand which inputs control the variability of their forecasts…reduce risks in the planning stage, prior to a costly implementation…and graphically communicate statistical information to clients, managers, and peers.

Designed for real-world application in today's complex business and engineering environments, Six Sigma Distribution Modeling will be invaluable to all practitioners who must select and apply advanced statistical modeling tools for accurate and precise Six Sigma analysis.

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 of training in countries around the world. Mr. Sleeper is also the author of Design For Six Sigma Statistics: 59 Tools for Diagnosing and Solving Problems in DFSS Initiatives, published by McGraw-Hill.