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Wiley & SAS Business Series

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:

  1. Activity-Based Management for Financial Institutions: Driving Bottom-Line Results by Brent Bahnub
  2. Branded! How Retailers Engage Consumers with Social Media and Mobility by Bernie Brennan and Lori Schafer
  3. Business Analytics for Customer Intelligence by Gert Laursen
  4. Business Analytics for Managers: Taking Business Intelligence beyond Reporting by Gert Laursen and Jesper Thorlund
  5. Business Intelligence Competency Centers: A Team Approach to Maximizing Competitive Advantage by Gloria J. Miller, Dagmar Brautigam, and Stefanie Gerlach
  6. Business Intelligence Success Factors: Tools for Aligning Your Business in the Global Economy by Olivia Parr Rud
  7. Case Studies in Performance Management: A Guide from the Experts by Tony C. Adkins
  8. CIO Best Practices: Enabling Strategic Value with Information Technology, Second Edition by Joe Stenzel
  9. Credit Risk Assessment: The New Lending System for Borrowers, Lenders, and Investors by Clark Abrahams and Mingyuan Zhang
  10. Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring by Naeem Siddiqi
  11. Customer Data Integration: Reaching a Single Version of the Truth, by Jill Dyche and Evan Levy
  12. Demand-Driven Forecasting: A Structured Approach to Forecasting by Charles Chase
  13. Enterprise Risk Management: A Methodology for Achieving Strategic Objectives by Gregory Monahan
  14. Executives Guide to Solvency II by David Buckham, Jason Wahl, and Stuart Rose
  15. Fair Lending Compliance: Intelligence and Implications for Credit Risk Management by Clark R. Abrahams and Mingyuan Zhang
  16. Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications by Robert Rowan
  17. Information Revolution: Using the Information Evolution Model to Grow Your Business by Jim Davis, Gloria J. Miller, and Allan Russell
  18. Manufacturing Best Practices: Optimizing Productivity and Product Quality by Bobby Hull.
  19. Marketing Automation: Practical Steps to More Effective Direct Marketing by Jeff LeSueur
  20. Mastering Organizational Knowledge Flow: How to Mae Knowledge Sharing Work by Frank Leistner
  21. Performance Management: Finding the Missing Pieces (to Close the Intelligence Gap) by Gary Cokins
  22. Performance Management: Integrating Strategy Execution, Methodologies, Risk, and Analytics by Gary Cokins
  23. Retail Analytics: The Secret Weapon by Emmett Cox
  24. Social Network Analysis in Telecommunications by Carlos Andre Reis Pinheiro
  25. The Business Forecasting Deal: Exposing Bad Practices and Providing Practical Solutions by Michael Gilliland
  26. The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher
  27. The Executives Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow
  28. The New Know: Innovation Powered by Analytics by Thornton May
  29. The Value of Business Analytics: Identifying the Path to Profitability by Evan Stubbs
  30. 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

Statistical Thinking

Improving Business Performance

Second Edition

Roger Hoerl

Ron Snee


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:

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:

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