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E-raamat: Six Sigma Distribution Modeling

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  • Formaat: 448 pages
  • Ilmumisaeg: 09-Jan-2007
  • Kirjastus: McGraw-Hill Professional
  • Keel: eng
  • ISBN-13: 9780071712491
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Six Sigma Distribution ModelingSuurem pilt
  • Formaat: 448 pages
  • Ilmumisaeg: 09-Jan-2007
  • Kirjastus: McGraw-Hill Professional
  • Keel: eng
  • ISBN-13: 9780071712491
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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.
Preface xi
Chapter 1 Modeling Random Behavior with Probability Distributions 1
1.1 Terminology to Describe Randomness
6
1.2 Selecting a Distribution Model
7
1.3 Selecting Candidate Distributions Using Theoretical Knowledge
11
1.4 Selecting a Distribution Family Using Graphical Tools
16
1.5 Selecting a Distribution Family Using Statistical Tools
26
1.5.1 Selecting a Tool for Testing a Distribution Model
27
1.5.2 Interpreting Goodness-of-Fit Test Results
30
1.5.3 Calculating Goodness-of-Fit Test Statistics
35
1.5.4 Understanding Goodness-of-Fit Tests
46
1.6 Selecting a Distribution Model with Expert Opinion
48
1.6.1 Truncating a Distribution Model
50
1.6.2 Modeling the Effects of Long-Term Variation
57
1.6.3 Applying Opinions in the Absence of Data and Theory
59
Chapter 2 Selecting Statistical Software Tools for Six Sigma Practitioners 63
2.1 Comparison of Descriptive Statistics Functions in Selected Statistical Software
66
2.2 Comparison of Distribution Functions in Selected Statistical Software
69
2.3 Defects and Limitations of Microsoft Office Excel Spreadsheet Software
76
Chapter 3 Applying Nonnormal Distribution Models in Six Sigma Projects 79
3.1 Assessing Process Stability
84
3.2 Measuring Process Capability of a Distribution
96
Chapter 4 Applying Distribution Models and Simulation in Six Sigma Projects 115
4.1 Understanding Monte Carlo Simulation
116
4.1.1 Recognizing Opportunities for Simulation
117
4.1.2 Defining Input Distributions and Output Variables for Crystal Ball Simulations
118
4.1.3 Identifying the Vital Few Inputs with Sensitivity Analysis
122
4.2 Case Study: Bank Loan Process Improvement
125
4.3 Case Study: Simulating and Optimizing a Model Built from a Designed Experiment
130
4.4 Case Study: Perishable Inventory Optimization
137
4.5 Benefits of Simulation and Optimization
144
Chapter 5 Glossary of Terms 145
Chapter 6 Bernoulli (Yes-No) Distribution Family 153
Chapter 7 Beta Distribution Family 157
Chapter 8 Binomial Distribution Family 169
Chapter 9 Chi-Squared Distribution Family 179
9.1 Chi Distribution Family
186
9.2 Noncentral Chi-Squared and Chi Distribution Families
194
Chapter 10 Discrete Uniform Distribution Family 199
Chapter 11 Exponential Distribution Family 203
11.1 Two-Parameter Exponential Distribution Family
211
11.2 Truncated Exponential Distribution Family
217
Chapter 12 Extreme Value (Gumbel) Distribution Family 223
12.1 Overview of Extreme Value Theory
233
Chapter 13 F Distribution Family 239
13.1 Noncentral F Distribution Family
243
Chapter 14 Gamma Distribution Family 245
14.1 Three-Parameter Gamma Distribution Family
256
Chapter 15 Geometric Distribution Family 261
Chapter 16 Hypergeometric Distribution Family 269
Chapter 17 Laplace Distribution Family 279
Chapter 18 Logistic Distribution Family 287
18.1 Loglogistic Distribution Family
294
Chapter 19 Lognormal Distribution Family 299
Chapter 20 Negative Binomial Distribution Family 307
Chapter 21 Normal (Gaussian) Distribution Family 317
21.1 Half-Normal Distribution Family
328
21.2 Truncated Normal Distribution Family
332
Chapter 22 Pareto Distribution Family 337
Chapter 23 Poisson Distribution Family 345
23.1 Right-Truncated Poisson Distribution Family
353
23.2 Positive (Zero-Truncated) Poisson Distribution Family
355
Chapter 24 Rayleigh Distribution Family 357
Chapter 25 Student's t Distribution Family 365
25.1 Noncentral t Distribution Family
372
Chapter 26 Triangular Distribution Family 375
Chapter 27 Uniform Distribution Family 383
Chapter 28 Weibull Distribution Family 391
28.1 Three-Parameter Weibull Distribution Family
403
References 409
Index 413


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