Statistical Thinking: Improving Business Performance

This innovative book teaches students to understand the strategic value of data and statistics in solving real business problems. Following principles of effective learning identified by educational and behavioral research, the instruction proceeds from tangible examples to abstract theory; from the big picture, or "whole," to details, or "parts"; and from a conceptual understanding to ability to perform specific tasks. While the computer is used for computational details, the authors describe the role of statistical thinking and methods for problem solving and process improvement to encourage use of the tools. Hoerl and Snee also teach skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, graphically analyzing data using basic tools, deriving actionable conclusions from data analyses, and understanding the limitations of statistical analyses. In summary, the authors demonstrate that statistical thinking and methodology can help students be more valuable and effective in their chosen careers.

Table of Contents:
Part I. Statistical Thinking Concepts. 1. The Need for Business Improvement. Overview. Today's Business Realities and the Need to Improve. We Now Have Two Jobs: A Model for Business Improvement. New Management Approaches Require Statistical Thinking. Principles of Statistical Thinking. Applications of Statistical Thinking. Summary of Key Points. Exercises. References. 2. The Overall Statistical Thinking Approach. Overview. Case Study: The Effect of Advertising on Sales. Case Study: Improvement of a Soccer Team's Performance. A Model for Statistical Thinking. Variation in Business Processes. The Synergy Between Data and Subject Matter Knowledge. The Dynamic Nature of Business Processes. Summary. Project Update. Exercises. References. 3. Understanding Business Processes. Overview. Examples of Business Processes. The SIPOC Model for Processes. Identifying Business Processes. Analysis of Business Processes. Systems of Processes. The Measurement Process. Summary. Project Update. Exercises. References. Part II. Improvement Strategies And Basic Tools. 4. Process Improvement And Problem-Solving Strategies. Overview. Case Study: Reducing Resin Output Variation. Case Study: Reducing Telephone Waiting Time at a Bank. The Process Improvement Strategy. Case Study: Resolving Customer Complaints of Baby Wipe Flushability. Case Study: The Realized Revenue Fiasco. The Problem-Solving Strategy. Six Sigma Process Improvement Strategy. Summary. Project Update. Exercises. References. Introduction to Microsoft Excel. 5. Process Improvement And Problem-Solving Tools. Introduction. Relationship of the Tools to the Strategies. Data Collection Tools. Data Analysis Tools. Knowledge-Based Tools. Summary. Project Update. Exercises. References. Part III. Formal Statistical Methods. Introduction to Minitab. Introduction to JMP. 6. Building And Using Models. Overview. Examples of Business Models. Types and Uses of Models. The Regression Modeling Process. Building Models With One Predictor Variable. Building Models With Several Predictor Variables. Multicollinearity, Another Model Check. Some Limitations of Using Existing Data. Summary. Project Update. Exercises. References. 7. Using Process Experimentation To Build Models. Overview. Why Do We Need a Statistical Approach?. Examples of Process Experiments. The Statistical Approach to Experimentation. Two-Factor Experiments: A Case Study. Three-Factor Experiments: A Case Study. Larger Experiments. Blocking, Randomization, and Center Points. Summary. Project Update. Exercises. References. 8. Applications Of Statistical Inference Tools. Overview. Examples of Statistical Inference Tools. The Process of Applying Statistical Inference. Statistical Confidence and Prediction Intervals. Statistical Hypothesis Tests. Test for Continuous Data. Test for Discrete Data. Test for Regression Analysis. Sample Size Formulas. Summary. Project Update. Exercises. References. 9. The Underlying Theory Of Statistical Inference. Overview. Applications of the Theory. The Theoretical Framework of Statistical Inference. Types of Data. Probability Distributions. Sampling Distributions. Linear Combinations. Transformations. Summary. Project Update. Exercises. References. 10. Summary And Path Forward. Overview. A Personal Case Study by Tom Pohlen. Case Study: Newspaper Accuracy. Review of the Statistical Thinking Approach. Text Summary. Potential Next Steps to Deeper Understanding of Statistical Thinking. Project Summary and De-Briefing. Exercises. References. Appendix A. Effective Teamwork. Appendix B. Presentations and Report Writing. Appendix C. More on Surveys. Appendix D. More on the Six Sigma Improvement Approach. Appendix E. More on Design of Experiments. Appendix F. More on Inference Tools. Appendix G. More on Probability Distributions. Appendix H. Process Design (Reengineering). Appendix I. t Critical Values. Appendix J. Standard Normal Probabilities (Cumulative z Curve Areas). Index.

Review :
"Hoerl and Snee have captured the essence of Six Sigma methods in their book "Statistical Thinking - Improving Business Performance." This is THE primer that should be mandatory reading for all Black Belts who really want to master the statistical elements of Six Sigma. I heartily recommend it also for Master Black Belts who must review in order to be able to conduct Black Belt training. This book is a key text for all professionals in Six Sigma!" "This book is clearly a groundbreaking work. To my knowledge there is nothing on the market that even comes close to doing what it does. I have always felt that one of the weaknesses of our teaching at the introductory level is giving the students a strategy for applying the statistical tools that they learn. This book does that beautifully." "This is a book that is badly needed with material that will make a real difference in the learning of students and their understanding and appreciation of statistics as a discipline and its application in solving real problems and improving real processes. The approach is one that students will enjoy. They will gain an appreciation for the discipline of statistics and see aspects of it that they never will appreciate from the typical introductory statistics text. It is a text that I would love to teach out of."