Title Details

ROGER HOERL leads the Applied Statistics Laboratory at GE Global Research, which focuses on new product and service development within each of the GE businesses. In 2006, he received the Coolidge Fellowship from GE Global Research, honoring one scientist a year from among the four global GE Research and Development sites for lifetime technical achievement. Dr. Hoerl has authored five books in the areas of statistics and business improvement, two book chapters, and over thirty-five refereed journal articles.

RON SNEE is founder and President of Snee Associates, an authority on designing and implementing improvement and cost reduction solutions for a variety of organizational environments. Dr. Snee has an outstanding record of leadership in process and organizational improvement in a variety of industries including pharmaceutical, biotech, clinical diagnostics, and telecommunications. Among his other achievements, he is credited with leading the design of the first company-wide continuous improvement curriculum for the global giant E. I. DuPont de Nemours. He holds a host of awards and honors, has coauthored four books, and published more than 200 articles on process improvement, quality, management, and statistics.

Introduction to JMP xvii

Part One Statistical Thinking Concepts 1

Chapter 1 Need for Business Improvement 3

Today’s Business Realities and the Need to Improve 4

We Now Have Two Jobs: A Model for Business Improvement 7

New Management Approaches Require Statistical Thinking 10

Principles of Statistical Thinking 15

Applications of Statistical Thinking 18

Summary 20

Notes 20

Chapter 2 Statistical Thinking Strategy 23

Case Study: The Effect of Advertising on Sales 24

Case Study: Improvement of a Soccer Team’s Performance 30

Statistical Thinking Strategy 39

Context of Statistical Thinking: Statistics Discipline as a System 43

Variation in Business Processes 45

Synergy between Data and Subject Matter Knowledge 50

Dynamic Nature of Business Processes 51

Summary 53

Project Update 53

Notes 54

Chapter 3 Understanding Business Processes 55

Examples of Business Processes 56

SIPOC Model for Processes 62

Identifying Business Processes 64

Analysis of Business Processes 65

Systems of Processes 79

Measurement Process 82

Summary 87

Project Update 88

Notes 89

Part Two Statistical Engineering: Frameworks and Basic Tools 91

Chapter 4 Statistical Engineering: Tactics to Deploy Statistical Thinking 93

Statistical Engineering 94

Case Study: Reducing Resin Output Variation 95

Case Study: Reducing Telephone Waiting Time at a Bank 101

Basic Process Improvement Framework 105

Case Study: Resolving Customer Complaints of Baby Wipe Flushability 111

Case Study: The Realized Revenue Fiasco 117

Basic Problem-Solving Framework 123

DMAIC Framework 128

DMAIC Case Study: Newspaper Accuracy 130

Summary 137

Project Update 137

Notes 138

Chapter 5 Process Improvement and Problem-Solving Tools 139

Stratification 141

Data Collection Tools 142

Basic Graphical Analysis Tools 156

Knowledge-Based Tools 172

Process Stability and Capability Tools 205

Summary 226

Project Update 227

Notes 227

Part Three Formal Statistical Methods 229

Chapter 6 Building and Using Models 231

Examples of Business Models 232

Types and Uses of Models 235

Regression Modeling Process 238

Building Models with One Predictor Variable 246

Building Models with Several Predictor Variables 254

Multicollinearity: Another Model Check 261

Some Limitations of Using Existing Data 264

Summary 265

Project Update 267

Notes 267

Chapter 7 Using Process Experimentation to Build Models 269

Why Do We Need a Statistical Approach? 270

Examples of Process Experiments 273

Statistical Approach to Experimentation 279

Two-Factor Experiments: A Case Study 286

Three-Factor Experiments: A Case Study 292

Larger Experiments 299

Blocking, Randomization, and Center Points 301

Summary 303

Project Update 304

Notes 305

Chapter 8 Applications of Statistical Inference Tools 307

Examples of Statistical Inference Tools 310

Process of Applying Statistical Inference 314

Statistical Confidence and Prediction Intervals 317

Statistical Hypothesis Tests 330

Tests for Continuous Data 339

Test for Discrete Data: Comparing Two or More Proportions 344

Test for Regression Analysis: Test on a Regression Coefficient 345

Sample Size Formulas 346

Summary 352

Project Update 353

Notes 353

Chapter 9 Underlying Theory of Statistical Inference 355

Applications of the Theory 356

Theoretical Framework of Statistical Inference 358

Types of Data 363

Probability Distributions 366

Sampling Distributions 382

Linear Combinations 389

Transformations 392

Summary 411

Project Update 411

Notes 412

Chapter 10 Summary and Path Forward 413

A Personal Case Study by Tom Pohlen 414

Review of the Statistical Thinking Approach 420

Text Summary 422

Potential Next Steps to Deeper Understanding of Statistical Thinking 425

Project Summary and Debriefing 427

Notes 427

Appendix A Effective Teamwork 429

Appendix B Presentations and Report Writing 439

Appendix C More on Surveys 445

Appendix D More on Regression 453

Appendix E More on Design of Experiments 467

Appendix F More on Inference Tools 479

Appendix G More on Probability Distributions 483

Appendix H Process Design (Reengineering) 491

Appendix I t Critical Values 497

Appendix J Standard Normal Probabilities (Cumulative z Curve Areas) 499

Index 503