Author:
Dey, Kieron A. author
Imprint:Boca Raton : CRC Press, c[2015].
Descriptionxi, 219 p. : ill. ; 24 cm.
Note:"A Productivity Book".
Note:Ch. 1 Simplicity of Statistical Design and Control -- 1.1. Making a Start -- 1.2. How Does It Work? -- 1.3. Care Management Case: Improving Health for Thousands of People -- 1.4. Discovery -- 1.5. Measurement Quality -- 1.6. Care Management Statistical Design -- 1.7. Baseline Data -- 1.8. Managing the Test -- 1.9. Test Results -- 1.10. Exploratory Analysis -- 1.11. What Might the Results Mean? -- 1.12. Findings Are Often Surprising -- 1.13. Significance of the Results -- 1.14. Implementation -- 1.15. Implementation Troubleshooting -- ch. 2 Designed Innovation -- 2.1. Innovation Uses More Right Brain than Left -- 2.2. Retailing Case: New Product Sales -- 2.3. Discovery -- 2.4. Measurement Quality -- 2.5. Preparing for the Test -- 2.6. Retail Furniture Statistical Design and Its Management -- 2.7. Exploratory Analysis and Inference -- 2.8. What Might the Results Mean? -- 2.9. Statistical Significance -- 2.10. Ironing Out Some Possible Wrinkles -- 2.11. Predicting and Delivering the Improvement -- 2.12. Retailing Designed Innovation Case: Conclusion -- ch. 3 Statistical Control -- 3.1. Using Statistical Control -- 3.2. Economic Advantage -- 3.3. Derivation -- 3.4. Practical Use of Statistical Control -- 3.5. Digression into Causality -- 3.6. Concluding Scientific Work in the Care Management Case -- 3.7. False Alarm Rate Is Neither Known Nor Useful in Statistical Control -- 3.8. Statistical Control Terminology -- 3.9. Statistics Breaks Down in Unstable Processes -- 3.10. Economic Loss without Statistical Control -- 3.11. Cost Explosion Story Unexploded -- 3.12. Tests for Statistical Control -- 3.13. Statistical Control Integrated with Statistical Design -- 3.14. Managing Statistical Control Schemes -- 3.15. Mechanics of Statistical Control -- 3.16. Where Did Statistical Control Originate? -- ch. 4 Measurement Error and Control -- 4.1. All Measurement Systems Are Inherently Flawed -- 4.2. Clinical Care Case: Initial Measurement Study and Long-Term Controls -- 4.3. Establishing a Measurement Control Scheme -- ch. 5 Statistical Design -- 5.1. Advantages of Large Statistical Design -- 5.2. Two-Level Designs -- 5.3. Full Factorial Designs -- 5.4. Fractional Factorial Designs -- 5.5. Backpacking Case -- 5.6. Discovery -- 5.7. Managing the Test -- 5.8. Measurement Quality -- 5.9. Exploratory Analysis -- 5.10. What Might the Initial Results Mean? -- 5.11. Exploring Interactions -- 5.12. Simpler Analysis -- 5.13. Statistical Significance -- 5.14. Solving the Puzzle -- 5.15. Aliasing -- 5.16. Analysis of All Pair Interactions -- 5.17. Measurement Problem Found and Fixed after the Test -- 5.18. Using Sales Change as the Test's Measurement -- 5.19. Calculating Precision and Sample Size Before the Test -- 5.20. Diagnosing Unusually High or Low Results in a Statistical Design Row -- 5.21. Guidance on Fractional Factorial Designs -- 5.22. Multifactorial Designs -- 5.23. Care Management Case: More Analytical Insight -- 5.24. Randomization -- 5.25. Milk Story -- 5.26. Soil Story -- 5.27. Geometric versus Nongeometric Designs -- 5.28. Aliasing Scheme for the Care Management Design -- 5.29. Augmenting Multifactorials to Also Estimate Pair Interactions -- 5.30. Testing Strategy -- 5.31. Uniqueness and Stumbling Around -- 5.32. Where Did Statistical Design Originate? -- ch. 6 Statistical Design and Control: A Dozen Large-Scale Case Studies -- 6.1. Selection of Cases -- ch. 7 Simultaneous Design -- 7.1. Solving Complex Problems Simply -- 7.2. Simultaneous Design Idea -- 7.3. Science Education Case -- 7.4. Discovery -- 7.5. Baseline Data -- 7.6. Simultaneous Statistical Designs for Science Classes -- 7.7. Pair Interactions across Designs and an Easier Analysis -- 7.8. Findings -- 7.9. Rules for Simultaneous Designs -- 7.10. General Multichannel Optimization Case -- 7.11. Simultaneous Design Procedure -- ch. 8 Scientific Method, Randomization, and Improvement Strategies -- 8.1. Simplicity of the Scientific Method -- 8.2. Scientific Method with Statistical Design and Control -- 8.3. Randomization Distribution -- 8.4. Randomization Device -- 8.5. Proof Isn't in the Pudding -- 8.6. What Science Lies beneath Implementation Being the Hardest Part? -- 8.7. Common Improvement Strategies -- 8.8. Randomized Control Trials (RCT) -- 8.9. Statistical Design and Control Are for Real Problems with Everyone Contributing -- ch. 9 Managing Improvement and Innovation -- 9.1. Organization -- 9.2. Speed without Net Resources -- 9.3. How to Manage Specific Improvements/Innovations -- 9.4. Statistical Design and Control Summary.
Note:Competitive Innovation and Improvement: Statistical Design and Control explains how to combine two widely known statistical methods-statistical design and statistical control - in a manner that can solve any business, government, or research problem quickly with sustained results. Because the problem-solving strategy employed is pure scientific method, it makes integration into any existing problem-solving or research method quite simple. The material in the book is presented in a manner that anyone can read and immediately put to use, including executives, managers, statisticians, scientists, engineers, researchers, and all of their supervisors and employees. Organizations can apply the concepts discussed with existing staff to release latent energy rather than adding to their workload. Optional footnotes provide the opportunity for more advanced technical insight. Supplying readers with an understanding of orthogonal design, the book illustrates key ideas through large-scale case studies. The book's 12 case studies examine the coupling of statistical design with economic control across a range of industries and problem types. The book suggests the real world, rather than mathematics alone, reveal how things work and how to make them work better. Book jacket.
