9781119605737

1119605733

9781119605720

1119605725

9781119605713

<P>Preface</p> <p>Introduction to JMP</p> <p><b>Part One: Statistical Thinking Concepts</b></p> <p><b>1. Need for Business Improvement</b></p> <p>a. Today's Business Realities and the Need to Improve</p> <p>b. We now Have Two Jobs: A model for Business Improvement</p> <p>c. New Management Approaches Require Statistical Thinking</p> <p>d. Principles of Statistical Thinking</p> <p>e. Applications of Statistical Thinking</p> <p>f. Summary and Looking Forward</p> <p>g. Notes</p> <p><b>2. Data: the Missing Link</b></p> <p>a. Why Do We Need Data?</p> <p>b. Types of Data</p> <p>c. All Data Are Not Created Equal</p> <p>d. Practical Sampling Tips to Ensure Data Quality</p> <p>e. What about Data Quantity?</p> <p>f. Documenting the Data Pedigree

The Data's Story</p> <p>g. The Measurement System</p> <p>h. Summarizing Data</p> <p>i. Summary and Looking Forward</p> <p>j. Notes</p> <p><b>3. Statistical Thinking Strategy</b></p> <p>a. Case Study: The Effect of Advertising on Sales</p> <p>b. Case Study: Improvement of a Soccer Team's Performance</p> <p>c. Statistical Thinking Strategy</p> <p>d. Variation in Business Processes</p> <p>e. Synergy between Data and Subject Matter Knowledge</p> <p>f. Dynamic Nature of Business Processes</p> <p>g. Value of Graphics

Discovering the Unexpected</p> <p>h. Summary and Looking Forward</p> <p>i. Project Update</p> <p>j. Notes</p> <p><b>4. Understanding Business Processes</b></p> <p>a. Examples of Business Processes</p> <p>b. SIPOC Model for Processes</p> <p>c. Identifying Business Processes</p> <p>d. Analysis of Business Processes</p> <p>e. Process Complexity</p> <p>f. The Hidden Plant

Another Source of Waste and Complexity</p> <p>g. Process Measurements</p> <p>h. Benchmarking</p> <p>i. Systems of Processes</p> <p>j. Summary and Looking Forward</p> <p>k. Project Update</p> <p>l. Notes</p> <p><b>Part Two: Holistic Improvement: Frameworks and Basic Tools</b></p> <p><b>5. Holistic Improvement: Tactics to Deploy Statistical Thinking</b></p> <p>a. Case Study: Revolving Customer Complaints of Baby Wipe Flushability</p> <p>b. The Problem-Solving Framework</p> <p>c. Case Study: Reducing Resin Output Variation</p> <p>d. The Process Improvement Framework</p> <p>e. Statistical Engineering</p> <p>f. Statistical Engineering Case Study: Predicting Corporate Defaults</p> <p>g. A Framework for Statistical Engineering Projects</p> <p>h. Summary and Looking Forward</p> <p>i. Project Update</p> <p>j. Notes</p> <p><b>6. Process Improvement and Problem-Solving Tools</b></p> <p>a. Practical Tools</p> <p>b. Knowledge-Based Tools</p> <p>c. Graphical Tools</p> <p>d. Analytical Tools</p> <p>e. Summary and Looking Forward</p> <p>f. Project Update</p> <p>g. Notes</p> <p><b>Part Three: Formal Statistical Methods</b></p> <p><b>7. Building and Using Models</b></p> <p>a. Examples of Business Models</p> <p>b. Types of Models</p> <p>c. Regression Modeling Process</p> <p>d. Building Models with One Predictor Variable</p> <p>e. Building Models with Several Predictor Variables</p> <p>f. Multicollinearity: Another Model Check</p> <p>g. Some Limitations of Using Observational Data</p> <p>h. Summary and Looking Forward</p> <p>i. Project Update</p> <p>j. Notes</p> <p><b>8. Using Process Experimentation to Build Models</b></p> <p>a. Randomized versus Observational Studies</p> <p>b. Why Do We Need a Statistical approach?</p> <p>c. Examples of Process Experiments</p> <p>d. Statistical Approach to Experimentation</p> <p>e. Two Factor Experiments: A Case Study</p> <p>f. Three Factor Experiments: A Case Study</p> <p>g. Larger Experiments</p> <p>h. Blocking, Randomization and Center Points</p> <p>i. Summary and Looking Forward</p> <p>j. Project Update</p> <p>k. Notes</p> <p><b>9. Applications of Statistical Inference Tools</b></p> <p>a. Examples of Statistical Inference Tools</p> <p>b. Process of Applying Statistical Inference</p> <p>c. Statistical Confidence and Prediction Intervals</p> <p>d. Statistical Hypothesis Tests</p> <p>e. Sample Size Formulas</p> <p>f. Summary and Looking Forward</p> <p>g. Project Update</p> <p>h. Notes</p> <p><b>10. Underlying Theory of Statistical Inference</b></p> <p>a. Applications of the Theory</p> <p>b. Theoretical Framework of Statistical Inference</p> <p>c. Probability Distributions</p> <p>d. Sampling Distributions</p> <p>e. Linear Combinations</p> <p>f. Transformations</p> <p>g. Summary and Looking Forward</p> <p>h. Project Update</p> <p>i. Notes</p> <p>Appendix A Effective Teamwork</p> <p>Appendix B Presentations and Report Writing</p> <p>Appendix C More on Surveys</p> <p>Appendix D More on Regression</p> <p>Appendix E More on Design of Experiments</p> <p>Appendix F More on Inference Tools</p> <p>Appendix G More on Probability Distributions</p> <p>Appendix H DMAIC Process Improvement Framework</p> <p>Appendix I T Critical Values</p> <p>Appendix J Standard Normal Probabilities (Cumulative Z Curve Areas)</p>