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by Leo Wright, Mia L. Stephens, Philip J. Ramsey, Marie A. Gaudard, Ian Cox
Visual Six Sigma: Making Data Analysis Lean
Copyright
Wiley & SAS Business Series
Preface
Acknowledgments
Background
Introduction
What Is Visual Six Sigma?
Moving beyond Traditional Six Sigma
Making Data Analysis Lean
Requirements of the Reader
Six Sigma and Visual Six Sigma
Background: Models, Data, and Variation
Six Sigma
Variation and Statistics
Making Detective Work Easier through Dynamic Visualization
Visual Six Sigma: Strategies, Process, Roadmap, and Guidelines
Conclusion
Notes
A First Look at JMP®
The Anatomy of JMP
Visual Displays and Analyses Featured in the Case Studies
Scripts
Personalizing JMP
Visual Six Sigma Data Analysis Process and Roadmap
Techniques Illustrated in the Case Studies
Conclusion
Notes
Case Studies
Reducing Hospital Late Charge Incidents
Framing the Problem
Collecting Data
Uncovering Relationships
Uncovering the Hot Xs
Identifying Projects
Conclusion
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
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
Note
Informing Pharmaceutical Sales and Marketing
Setting the Scene
Collecting the Data
Validating and Scoping the Data
Investigating Promotional Activity
A Deeper Understanding of Regional Differences
Summary
Conclusion
Additional Details
Note
Improving a Polymer Manufacturing Process
Setting the Scene
Framing the Problem
Reviewing Historical Data
Measurement System Analysis
Uncovering Relationships
Modeling Relationships
Revising Knowledge
Utilizing Knowledge
Conclusion
Note
Classification of Cells
Setting the Scene
Framing the Problem and Collecting the Data: The Wisconsin Breast Cancer Diagnostic Data Set
Uncovering Relationships
Constructing the Training, Validation, and Test Sets
Modeling Relationships: Logistic Model
Modeling Relationships: Recursive Partitioning
Modeling Relationships: Neural Net Models
Comparison of Classification Models
Conclusion
Notes
Index
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