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by Mia L. Stephens, Marie A. Gaudard, Ian Cox
Visual Six Sigma, 2nd Edition
Wiley & SAS Business Series
Title Page
Copyright
Preface to the Second Edition
Preface to the First Edition
Acknowledgments
About the Authors
Part One: Background
Chapter 1: Introduction
What Is Visual Six Sigma?
Chapter 2: Six Sigma and Visual Six Sigma
Background: Models, Data, and Variation
Models
Measurements
Observational versus Experimental Data
Six Sigma
Variation and Statistics
Making Detective Work Easier through Dynamic Visualization
Visual Six Sigma: Strategies, Process, Roadmap, and Guidelines
Conclusion
Notes
Chapter 3: A First Look at JMP
The Anatomy of JMP
Visual Displays and Analyses Featured in the Book
Scripts
Personalizing JMP
Visual Six Sigma Data Analysis Process and Roadmap
Techniques Illustrated in the Remaining Chapters
Conclusion
Notes
Chapter 4: Managing Data and Data Quality
Data Quality for Visual Six Sigma
The Collect Data Step
Example 1: Domestic Power Consumption
Example 2: Biscuit Sales
Conclusion
Notes
Part Two: Case Studies
Chapter 5: Reducing Hospital Late Charge Incidents
Framing the Problem
Collecting Data
Uncovering Relationships
Uncovering the Hot Xs
Identifying Projects
Conclusion
Chapter 6: Transforming Pricing Management in a Chemical Supplier
Setting the Scene
Framing the Problem: Understanding the Current State Pricing Process
Collecting Baseline Data
Uncovering Relationships
Modeling Relationships
Revising Knowledge
Utilizing Knowledge: Sustaining the Benefits
Conclusion
Chapter 7: Improving the Quality of Anodized Parts
Setting the Scene
Framing the Problem
Collecting Data
Uncovering Relationships
Locating the Team on the VSS Roadmap
Modeling Relationships
Revising Knowledge
Utilizing Knowledge
Conclusion
Notes
Chapter 8: Informing Pharmaceutical Sales and Marketing
Setting the Scene
Collecting the Data
Validating and Scoping the Data
Uncovering Relationships
Investigating Promotional Activity
A Deeper Understanding of Regional Differences
Summary
Conclusion
Note
Chapter 9: Improving a Polymer Manufacturing Process
Setting the Scene
Framing the Problem
Reviewing Historical Data
Measurement System Analysis (MSA)
Uncovering Relationships
Modeling Relationships
Revising Knowledge
Utilizing Knowledge
Conclusion
Notes
Chapter 10: Classification of Cells
Setting the Scene
Framing the Problem and Collecting the Data: The Wisconsin Breast Cancer Diagnostic Data Set
Initial Data Exploration
Constructing the Training, Validation, and Test Sets
Prediction Models
Recursive Partitioning
Stepwise Logistic Model
Generalized Regression
Neural Net Models
Comparison of Classification Models
Conclusion
Notes
Part Three: Supplementary Material
Chapter 11: Beyond “Point and Click” with JMP
Programming and Application Building in JMP
A Motivating Example: Democracy and Trade Policy
Building the Missing Data Application
Conclusion
Notes
Index
End User License Agreement
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