Visual Six Sigma, Second Edition, Making Data Analysis Lean by Cox, Ian -- Read -- Imperial Library of Trantor

Index

Cover Wiley & SAS Business Series Title Page Copyright Table of Contents 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