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CITATION

Sleeper, Andrew D.. Six Sigma Distribution Modeling. US: McGraw-Hill Education, 2007.

Six Sigma Distribution Modeling

Authors: Andrew D. Sleeper

Published:  2007

ISBN: 9780071712491 0071482784
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  • Contents
  • Preface
  • Chapter 1 Modeling Random Behavior with Probability Distributions
  • 1.1 Terminology to Describe Randomness
  • 1.2 Selecting a Distribution Model
  • 1.3 Selecting Candidate Distributions Using Theoretical Knowledge
  • 1.4 Selecting a Distribution Family Using Graphical Tools
  • 1.5 Selecting a Distribution Family Using Statistical Tools
  • 1.5.1 Selecting a Tool for Testing a Distribution Model
  • 1.5.2 Interpreting Goodness-of-Fit Test Results
  • 1.5.3 Calculating Goodness-of-Fit Test Statistics
  • 1.5.4 Understanding Goodness-of-Fit Tests
  • 1.6 Selecting a Distribution Model with Expert Opinion
  • 1.6.1 Truncating a Distribution Model
  • 1.6.2 Modeling the Effects of Long-Term Variation
  • 1.6.3 Applying Opinions in the Absence of Data and Theory
  • Chapter 2 Selecting Statistical Software Tools for Six Sigma Practitioners
  • 2.1 Comparison of Descriptive Statistics Functions in Selected Statistical Software
  • 2.2 Comparison of Distribution Functions in Selected Statistical Software
  • 2.3 Defects and Limitations of Microsoft Office Excel Spreadsheet Software
  • Chapter 3 Applying Nonnormal Distribution Models in Six Sigma Projects
  • 3.1 Assessing Process Stability
  • 3.2 Measuring Process Capability of a Distribution
  • Chapter 4 Applying Distribution Models and Simulation in Six Sigma Projects
  • 4.1 Understanding Monte Carlo Simulation
  • 4.1.1 Recognizing Opportunities for Simulation
  • 4.1.2 Defining Input Distributions and Output Variables for Crystal Ball Simulations
  • 4.1.3 Identifying the Vital Few Inputs with Sensitivity Analysis
  • 4.2 Case Study: Bank Loan Process Improvement
  • 4.3 Case Study: Simulating and Optimizing a Model Built from a Designed Experiment
  • 4.4 Case Study: Perishable Inventory Optimization
  • 4.5 Benefits of Simulation and Optimization
  • Chapter 5 Glossary of Terms
  • Chapter 6 Bernoulli (Yes-No) Distribution Family
  • Chapter 7 Beta Distribution Family
  • Chapter 8 Binomial Distribution Family
  • Chapter 9 Chi-Squared Distribution Family
  • 9.1 Chi Distribution Family
  • 9.2 Noncentral Chi-Squared and Chi Distribution Families
  • Chapter 10 Discrete Uniform Distribution Family
  • Chapter 11 Exponential Distribution Family
  • 11.1 Two-Parameter Exponential Distribution Family
  • 11.2 Truncated Exponential Distribution Family
  • Chapter 12 Extreme Value (Gumbel) Distribution Family
  • 12.1 Overview of Extreme Value Theory
  • Chapter 13 F Distribution Family
  • 13.1 Noncentral F Distribution Family
  • Chapter 14 Gamma Distribution Family
  • 14.1 Three-Parameter Gamma Distribution Family
  • Chapter 15 Geometric Distribution Family
  • Chapter 16 Hypergeometric Distribution Family
  • Chapter 17 Laplace Distribution Family
  • Chapter 18 Logistic Distribution Family
  • 18.1 Loglogistic Distribution Family
  • Chapter 19 Lognormal Distribution Family
  • Chapter 20 Negative Binomial Distribution Family
  • Chapter 21 Normal (Gaussian) Distribution Family
  • 21.1 Half-Normal Distribution Family
  • 21.2 Truncated Normal Distribution Family
  • Chapter 22 Pareto Distribution Family
  • Chapter 23 Poisson Distribution Family
  • 23.1 Right-Truncated Poisson Distribution Family
  • 23.2 Positive (Zero-Truncated) Poisson Distribution Family
  • Chapter 24 Rayleigh Distribution Family
  • Chapter 25 Student’s t Distribution Family
  • 25.1 Noncentral t Distribution Family
  • Chapter 26 Triangular Distribution Family
  • Chapter 27 Uniform Distribution Family
  • Chapter 28 Weibull Distribution Family
  • 28.1 Three-Parameter Weibull Distribution Family
  • References
  • Index