The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

Titles in the Wiley and SAS Business Series include:

*Activity-Based Management for Financial Institutions: Driving Bottom-Line Results*by Brent Bahnub*Branded! How Retailers Engage Consumers with Social Media and Mobility*by Bernie Brennan and Lori Schafer*Business Analytics for Customer Intelligence*by Gert Laursen*Business Analytics for Managers: Taking Business Intelligence beyond Reporting*by Gert Laursen and Jesper Thorlund*Business Intelligence Competency Centers: A Team Approach to Maximizing Competitive Advantage*by Gloria J. Miller, Dagmar Brautigam, and Stefanie Gerlach*Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy*by Olivia Parr Rud*Case Studies in Performance Management: A Guide from the Experts*by Tony C. Adkins*CIO Best Practices: Enabling Strategic Value with Information Technology, Second Edition*by Joe Stenzel*Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors*by Clark Abrahams and Mingyuan Zhang*Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring*by Naeem Siddiqi*Customer Data Integration: Reaching a Single Version of the Truth*, by Jill Dyche and Evan Levy*Demand-Driven Forecasting: A Structured Approach to Forecasting*by Charles Chase*Enterprise Risk Management: A Methodology for Achieving Strategic Objectives*by Gregory Monahan*Executive*’*s Guide to Solvency II*by David Buckham, Jason Wahl, and Stuart Rose*Fair Lending Compliance: Intelligence and Implications for Credit Risk Management*by Clark R. Abrahams and Mingyuan Zhang- Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications by Robert Rowan
*Information Revolution: Using the Information Evolution Model to Grow Your Business*by Jim Davis, Gloria J. Miller, and Allan Russell*Manufacturing Best Practices: Optimizing Productivity and Product Quality*by Bobby Hull.*Marketing Automation: Practical Steps to More Effective Direct Marketing*by Jeff LeSueur*Mastering Organizational Knowledge Flow: How to Mae Knowledge Sharing Work*by Frank Leistner*Performance Management: Finding the Missing Pieces (to Close the Intelligence Gap)*by Gary Cokins*Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics*by Gary Cokins*Retail Analytics: The Secret Weapon*by Emmett Cox*Social Network Analysis in Telecommunications*by Carlos Andre Reis Pinheiro*The Business Forecasting Deal: Exposing Bad Practices and Providing Practical Solutions*by Michael Gilliland*The Data Asset: How Smart Companies Govern Their Data for Business Success*by Tony Fisher*The Executive*’*s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business*by David Thomas and Mike Barlow*The New Know: Innovation Powered by Analytics*by Thornton May*The Value of Business Analytics: Identifying the Path to Profitability*by Evan Stubbs*Visual Six Sigma: Making Data Analysis Lean*by Ian Cox, Marie A. Gaudard, Philip J. Ramsey, Mia L. Stephens, and Leo Wright

For more information on any of the above titles, please visit www.wiley.com.

**Second Edition**

Copyright © 2012 by Roger W. Hoerl and Ronald D. Snee. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

The first edition of this book was *Statistical Thinking: Improving Business Performance*, Roger Hoerl and Ronald D. Snee, Duxbury Press, 2002 (0-534-38158-8).

Published simultaneously in Canada.

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ISBN 978-1-118-09477-8 (cloth); ISBN 978-1-118-2233-83 (ebk.);

ISBN 978-1-118-23685-7 (ebk.); ISBN 978-1-118-826185-9 (ebk.)

*To the memory of Arthur E. Hoerl, Horace P. Andrews, and Ellis R. Ott—great teachers from whom we learned much about the theory and use of statistical thinking*

Since the 1980s, statistical thinking has been discussed in the literature, applied in the workplace, and formally taught at such universities as Arizona State, Florida State, Delaware, Brigham Young, and Drexel, to name a few. Many academics and practitioners contributed to this development. While there has been some resistance from those preferring a more traditional, mathematically oriented approach, the profession has gradually accepted the need for readers to think deeply before calculating. A major milestone in the development of the concept of statistical thinking was the 2002 publication of the first textbook on the topic, *Statistical Thinking; Improving Business Performance*.

In the 10 years that followed the first edition, further evidence suggests that the principles upon which we based the first edition are valid. We have been particularly pleased that such leaders of the statistics profession as G. Rex Bryce of Brigham Young University and Bob Rodriquez of SAS—who recently served as president of the American Statistical Association—have publicly supported the approach. Perhaps the greatest compliment we received was from the journal, *Technometrics*, jointly published by the American Statistical Association and the American Society for Quality, which stated that *Statistical Thinking* was “probably the most practical basic statistics textbook that has ever been written within a business context.”

While both proponents and critics have noted that *Statistical Thinking* is radically different from the traditional, formula-based introductory statistics text on virtually every dimension, the major principles on which we based our unique approach are:

*Emphasis on dynamic processes being the context of sampling in real applications, rather than static populations, such as the infamous urns of colored balls discussed in so many statistics texts.*Ours was the first and still virtually the only text that discusses the need to document and study the process from which the data come in order to understand the pedigree of the data. Data cannot be effectively turned into information and understanding unless we properly understand their context and pedigree. We repeatedly remind the reader to verify the stability of the process before performing formal statistical analyses, as opposed to the typical text that simply assumes a random sample from a static population.*Discussion of the “big picture”—conceptual understanding of what we are actually trying to accomplish, prior to teaching tools and formulas.*We have found that students struggle with the formulas until they understand why they need this tool in the first place. For example, we discuss the need for business improvement, its history, and how to integrate various tools into an overall improvement approach—what we now refer to as statistical engineering, prior to presenting individual tools. We focus first on understanding of fundamental concepts, such as the omnipresence of variation, the value of sequential studies, data quality versus quantity, and the integration of data analysis with subject-matter knowledge via the scientific method. The methods are then presented within this conceptual context. Formulas are only presented when they offer some intuitive insight, as opposed to being an end in themselves.*Contrary to the typical approach of presenting theory and providing an illustration of the mathematics with a contrived example, we provide real, multistep improvement case studies and explain the theory behind these case studies.*We have found that people learn much easier going from the tangible to the abstract, as opposed to abstract to tangible. We know of no other textbook that provides such an emphasis on real case studies that require multiple tools to be properly lined and sequenced in order to solve a real problem.*Taking the emphasis on business statistics seriously and providing numerous real business problems and real business data sets.*We have found that many business statistics texts are virtually identical to engineering, social science, or general statistics texts, except that they add contrived business examples. Our case studies, examples, and even questions at the end of chapters are based on our decades of experience in the business community. One professor noted that his class learned as much about business as they did about statistics from the book. We consider that a very strong compliment.

While the fundamental principles of statistical thinking remain valid, much has changed since the first edition of *Statistical Thinking* was published. For example, the discipline of statistical engineering has emerged, which helps integrate the concepts of statistical thinking with the methods and tools. JMP, the statistical discovery software, has further established itself as a market leader among statistical applications accessible to a general audience. (See our introduction to JMP that follows.) In addition, since the first edition was published we have received a great deal of constructive criticism and suggestions for improvement, in terms of both content and organization and sequencing of topics. We have therefore written the second edition to practice continuous improvement by implementing improvement ideas suggested by readers, as well as to update the text so it is more relevant to today’s readers.

Perhaps the most significant enhancement we have made is to the content and flow of Chapter 5, where we present the basic graphical tools, knowledge-based tools, as well as process stability and capability. We trust that readers will find Chapter 5 clearer and easier to follow now. In the first edition, we presented these tools in alphabetical order, mainly because their typical sequence of application was provided in the process improvement and problem-solving frameworks, which we presented in Chapter 4. For the second edition, we followed the suggestions of several colleagues who taught from and used the first edition, and we totally rewrote the chapter. We present the tools in a more logical sequence—the sequence in which they are typically applied in practice. We also added tools, such as failure mode and effects analysis (FMEA) and the cause-and-effect (C&E) matrix, and provide further guidance on how the tools are naturally linked and sequenced. Process capability and stability are broken out into a separate section, as they are more detailed and quantitative than the other tools in this chapter.

In Chapter 4 we have also included a discussion of the modern discipline of statistical engineering and how it relates to statistical thinking and tools. While much has been written on this topic, to date there are no texts that discuss it. In addition to presenting the process-improvement and problem-solving frameworks as vehicles to integrate and sequence the tools, we have added the Define, Measure, Analyze, Improve, Control (DMAIC) framework made popular through the Lean Six Sigma initiative. We moved the newspaper publishing case study, originally in Chapter 10, to Chapter 4, as an example of the DMAIC framework. Within the context of statistical engineering, we emphasize that there are many ways to integrate and sequence the tools to attack large, complex, unstructured problems.

Other enhancements to the second edition include:

- Material added on regression analysis as Appendix D, including on the use of dummy variables to include discrete variables in regression models. This was requested by several people who used the first edition.
- New exercises for selected chapters available on the Wiley web site for the second edition.
- More modern data sets, such as an updated history of the Dow Jones industrial average.
- More detailed explanation of how to use JMP to apply the statistical tools on real problems.

We trust that readers will find the second edition to be an example of the application of statistical thinking to improve a textbook.