Table of Contents

Preface. Acknowledgements. Part I: Background. Chapter 1 Introduction. What Is Visual Six Sigma? Moving Beyond Traditional Six Sigma. Making Data Analysis Lean. Requirements of the Reader. Chapter 2 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. Chapter 3 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 Case Studies. Conclusion. Notes. Part II: Case Studies. Chapter 4 Reducing Hospital Late Charge Incidents. Framing the Problem. Collecting Data. Uncovering Relationships. Uncovering the Hot Xs. Identifying Projects. Conclusion. Chapter 5 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 6 Improving the Quality of Anodized Parts. Setting the Scene. Framing the Problem. Collecting Data. Uncovering Relationships. Finding the Team on the VSS Roadmap. Modeling Relationships. Revise Knowledge. Utilizing Knowledge. Conclusion. Notes. Chapter 7 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. Notes. Chapter 8 Improving a Polymer Manufacturing Process. Setting the Scene. Framing the Problem. Reviewing Historical Data. Measurement Systems Analysis. Uncovering Relationships. Modeling Relationships. Revising Knowledge. Utilizing Knowledge. Conclusion. Note. Chapter 9 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.

About the Author

Ian Cox, PhD , is Solutions Manager for JMP Sales and Marketing. He has worked for Digital Equipment Corporation, Motorola, and Motorola University and is a Six Sigma Black Belt. Marie A. Gaudard, PhD , is a Partner with the North Haven Group and an Emerita Professor of Statistics at the University of New Hampshire. She has worked extensively as a teacher and consultant in industry, focusing on statistical quality improvement, predictive modeling, and data analysis. Philip J. Ramsey, PhD , is a Partner with the North Haven Group and a member of the statistics faculty at the University of New Hampshire. He is an industrial statistician with extensive experience in applying statistical methods to products, processes, and research and development programs. Mia L. Stephens, MS , is an Academic Ambassador with the JMP division of SAS. Formerly a trainer, consultant, North Haven Group partner, and statistics instructor at the University of New Hampshire, she is an expert in Lean Six Sigma and Design for Six Sigma program deployment. Leo T. Wright is Product Manager of Six Sigma and Quality Solutions for the JMP division of SAS. He has worked for several Fortune 500 manufacturing organizations and is a Six Sigma Black Belt and an ASQ Certified Quality Engineer.