Visual Six Sigma: Making Data Analysis Lean by Ian Cox, Marie A. Gaudard, Philip J. Ramsey, Mia L. Stephens | | Hardcover | Barnes & Noble
Visual Six Sigma: Making Data Analysis Lean (Wiley and SAS Business Series) / Edition 1
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Visual Six Sigma: Making Data Analysis Lean (Wiley and SAS Business Series) / Edition 1

by Ian Cox, Marie A. Gaudard, Philip J. Ramsey, Mia L. Stephens
     
 

ISBN-10: 0470506911

ISBN-13: 9780470506912

Pub. Date: 12/21/2009

Publisher: Wiley

Because of its unique visual emphasis, Visual Six Sigma opens the doors for you to take an active role in data-driven decision making, empowering you to leverage your contextual knowledge to pose relevant questions and make sound decisions.

This book shows you how to leverage dynamic visualization and exploratory data analysis techniques to:

  • See

Overview

Because of its unique visual emphasis, Visual Six Sigma opens the doors for you to take an active role in data-driven decision making, empowering you to leverage your contextual knowledge to pose relevant questions and make sound decisions.

This book shows you how to leverage dynamic visualization and exploratory data analysis techniques to:

  • See the sources of variation in your data
  • Search for clues in your data to construct hypotheses about underlying behavior
  • Identify key drivers and models
  • Shape and build your own real-world Six Sigma experience

Whether you 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 strategies, process, and road map to put Visual Six Sigma to work for your company.

Broaden and deepen your implementation of Visual Six Sigma with the intuitive and easy-to-use tools found in Visual Six Sigma: Making Data Analysis Lean.

Product Details

ISBN-13:
9780470506912
Publisher:
Wiley
Publication date:
12/21/2009
Series:
Wiley and SAS Business Series, #28
Pages:
492
Product dimensions:
7.30(w) x 10.10(h) x 1.80(d)

Table of Contents

Preface ix

Acknowledgments xi

PART I BACKGROUND

CHAPTER 1 Introduction 3

What Is Visual Six Sigma? 3

Moving beyond Traditional Six Sigma 4

Making Data Analysis Lean 4

Requirements of the Reader 5

CHAPTER 2 Six Sigma and Visual Six Sigma 7

Background: Models, Data, and Variation 7

Six Sigma 10

Variation and Statistics 13

Making Detective Work Easier through Dynamic Visualization 14

Visual Six Sigma: Strategies, Process, Roadmap, and Guidelines 16

Conclusion 21

Notes 21

CHAPTER 3 A First Look at JMP® 23

The Anatomy of JMP 23

Visual Displays and Analyses Featured in the Case Studies 39

Scripts 44

Personalizing JMP 47

Visual Six Sigma Data Analysis Process and Roadmap 47

Techniques Illustrated in the Case Studies 50

Conclusion 50

Notes 50

PART II CASE STUDIES

CHAPTER 4 Reducing Hospital Late Charge Incidents 57

Framing the Problem 58

Collecting Data 59

Uncovering Relationships 62

Uncovering the Hot Xs 90

Identifying Projects 103

Conclusion 103

CHAPTER 5 Transforming Pricing Management in a Chemical Supplier 105

Setting the Scene 106

Framing the Problem: Understanding the Current

State Pricing Process 107

Collecting Baseline Data 112

Uncovering Relationships 121

Modeling Relationships 147

Revising Knowledge 152

Utilizing Knowledge: Sustaining the Benefits 159

Conclusion 162

CHAPTER 6 Improving the Quality of Anodized Parts 165

Setting the Scene 166

Framing the Problem 167

Collecting Data 169

Uncovering Relationships 183

Locating the Team on the VSS Roadmap 196

Modeling Relationships 197

Revising Knowledge 210

Utilizing Knowledge 229

Conclusion 231

Note 232

CHAPTER 7 Informing Pharmaceutical Sales and Marketing 233

Setting the Scene 235

Collecting the Data 235

Validating and Scoping the Data 237

Investigating Promotional Activity 263

A Deeper Understanding of Regional Differences 282

Summary 291

Conclusion 292

Additional Details 292

Note 301

CHAPTER 8 Improving a Polymer Manufacturing Process 303

Setting the Scene 305

Framing the Problem 307

Reviewing Historical Data 314

Measurement System Analysis 320

Uncovering Relationships 334

Modeling Relationships 345

Revising Knowledge 366

Utilizing Knowledge 378

Conclusion 388

Note 389

CHAPTER 9 Classification of Cells 391

Setting the Scene 393

Framing the Problem and Collecting the Data: The Wisconsin Breast Cancer Diagnostic Data Set 394

Uncovering Relationships 395

Constructing the Training, Validation, and Test Sets 417

Modeling Relationships: Logistic Model 443

Modeling Relationships: Recursive Partitioning 460

Modeling Relationships: Neural Net Models 467

Comparison of Classification Models 480

Conclusion 483

Notes 484

Index 485

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