Statistical Thinking: Improving Business Performance

Abstract

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.

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