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Visual Six Sigma, 2nd Edition

Book Description

Streamline data analysis with an intuitive, visual Six Sigma strategy

Visual Six Sigma provides the statistical techniques that help you get more information from your data. A unique emphasis on the visual allows you to take a more active role in data-driven decision making, so you can leverage your contextual knowledge to pose relevant questions and make more sound decisions. You'll learn dynamic visualization and exploratory data analysis techniques that help you identify occurrences and sources of variation, and the strategies and processes that make Six Sigma work for your organization.

The Six Sigma strategy helps you identify and remove causes of defects and errors in manufacturing and business processes; the more pragmatic Visual approach opens the strategy beyond the realms of statisticians to provide value to all business leaders amid the growing need for more accessible quality management tools.

  • See where, why, and how your data varies
  • Find clues to underlying behavior in your data
  • Identify key models and drivers
  • Build your own Six-Sigma experience

Whether your work involves a Six Sigma improvement project, a design project, a data-mining inquiry, or a scientific study, this practical breakthrough guide equips you with the skills and understanding to get more from your data. With intuitive, easy-to-use tools and clear explanations, Visual Six Sigma is a roadmap to putting this strategy to work for your company.

Table of Contents

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