Statistical Thinking: Improving Business Performance / Edition 2

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Overview

Now in a second edition, Roger Hoerl and Ron Snee's StatisticalThinking: Improving Business Performance prepares you for businessleadership by developing your capacity to apply statisticalthinking to improve business processes. The authors clearlyillustrate how to implement statistical thinking and methodology inyour work to enhance business performance.

Introducing statistical engineering, a discipline that helps youutilize multiple statistical tools in a logical way to drivegreater impact, the new edition of Statistical Thinking: ImprovingBusiness Performance now:

  • Emphasizes JMP software as the main tool to develop graphics andperform statistical analyses
  • Explores basic graphical tools, knowledge-based tools, as wellas process stability and capability
  • Discusses the tools in a more logical sequence—the sequencein which they are typically applied in practice
  • Adds tools, such as failure mode and effects analysis (FMEA) andthe cause and effect (C&E) matrix, as well as further guidanceon how the tools are naturally linked and sequenced
  • Looks at the Define, Measure, Analyze, Improve, Control (DMAIC)framework made popular through the Lean Six Sigma initiative

This Second Edition offers an abundance of newexercises—available on the new edition's companionwebsite—and real case studies for an increasingly relevantpresentation of statistics in the business setting. Whether you area business performance manager, cost accountant, or a student ofstatistics, Statistical Thinking, Second Edition shows you how tosolve real problems, boost real processes, and successfully deriveactionable conclusions from data analysis.

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Product Details

  • ISBN-13: 9781118094778
  • Publisher: Wiley
  • Publication date: 4/24/2012
  • Series: Wiley and SAS Business Series , #48
  • Edition description: New Edition
  • Edition number: 2
  • Pages: 544
  • Sales rank: 348,240
  • Product dimensions: 7.28 (w) x 9.98 (h) x 1.69 (d)

Meet the Author

ROGER HOERL leads the Applied Statistics Laboratory at GE GlobalResearch, which focuses on new product and service developmentwithin each of the GE businesses. In 2006, he received the CoolidgeFellowship from GE Global Research, honoring one scientist a yearfrom among the four global GE Research and Development sites forlifetime technical achievement. Dr. Hoerl has authored five booksin the areas of statistics and business improvement, two bookchapters, and over thirty-five refereed journal articles.

RON SNEE is founder and President of Snee Associates, anauthority on designing and implementing improvement and costreduction solutions for a variety of organizational environments.Dr. Snee has an outstanding record of leadership in process andorganizational improvement in a variety of industries includingpharmaceutical, biotech, clinical diagnostics, andtelecommunications. Among his other achievements, he is creditedwith leading the design of the first company-wide continuousimprovement curriculum for the global giant E. I. DuPont deNemours. He holds a host of awards and honors, has coauthored fourbooks, and published more than 200 articles on process improvement,quality, management, and statistics.

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Table of Contents

Preface xiii

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 Performance30

Statistical Thinking Strategy 39

Context of Statistical Thinking: Statistics Discipline as aSystem 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 Tools91

Chapter 4 Statistical Engineering: Tactics to DeployStatistical 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 WipeFlushability 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 Tools139

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 Models269

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 Tools307

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 Proportions344

Test for Regression Analysis: Test on a Regression Coefficient345

Sample Size Formulas 346

Summary 352

Project Update 353

Notes 353

Chapter 9 Underlying Theory of Statistical Inference355

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 StatisticalThinking 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 zCurve Areas) 499

Index 503

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