This useful guide concisely communicates ten practical statistical methods that are widely applicable to research, product design, process design, validation, manufacturing, and continuous improvement in many different industries. The coverage presents the key ideas behind each statistical method in clear, concise, and accessible writing, with minimal mathematical detail. Focusing on practical aspects for problem-solving, the text illustrates applications with numerous examples, providing a accessible resource to scientists, engineers, and other technical personnel in R&D and manufacturing, as well as quality professionals, analytical chemists, and technical managers in industry, universities, and academia.
ANAND M. JOGLEKAR, PhD, is a leading statistics educator and consultant. In 1990, Dr. Joglekar founded Joglekar Associates, a firm dedicated to helping industrial organizations reach their goals through the effective implementation of statistical methods. He has taught statistical methods to thousands of industry participants through in-house seminars and seminars sponsored by associations such as the LifeScience Alley(R), Institute of Food Technologists, and American Association of Cereal Chemists. Among his many publications, Dr. Joglekar is the author of Statistical Methods for Six Sigma in R&D and Manufacturing (also from Wiley).
PREFACE. 1. BASIC STATISTICS: HOW TO REDUCE FINANCIAL RISK? 1.1. Capital Market Returns. 1.2. Sample Statistics. 1.3. Population Parameters. 1.4. Confidence Intervals and Sample Sizes. 1.5. Correlation. 1.6. Portfolio Optimization. 1.7. Questions to Ask. 2. WHY NOT TO DO THE USUAL t-TEST AND WHAT TO REPLACE IT WITH? 2.1. What is a t-Test and what is Wrong with It? 2.2. Confidence Interval is Better Than a t-Test. 2.3. How Much Data to Collect? 2.4. Reducing Sample Size. 2.5. Paired Comparison. 2.6. Comparing Two Standard Deviations. 2.7. Recommended Design and Analysis Procedure. 2.8. Questions to Ask. 3. DESIGN OF EXPERIMENTS: IS IT NOT GOING TO COST TOO MUCH AND TAKE TOO LONG? 3.1. Why Design Experiments? 3.2. Factorial Designs. 3.3. Success Factors. 3.4. Fractional Factorial Designs. 3.5. Plackett Burman Designs. 3.6. Applications. 3.7. Optimization Designs. 3.8. Questions to Ask. 4. WHAT IS THE KEY TO DESIGNING ROBUST PRODUCTS AND PROCESSES? 4.1. The Key to Robustness. 4.2. Robust Design Method. 4.3. Signal-to-Noise Ratios. 4.4. Achieving Additivity. 4.5. Alternate Analysis Procedure. 4.6. Implications for R&D. 4.7. Questions to Ask. 5. SETTING SPECIFICATIONS: ARBITRARY OR IS THERE A METHOD TO IT? 5.1. Understanding Specifications. 5.2. Empirical Approach. 5.3. Functional Approach. 5.4. Minimum Life Cycle Cost Approach. 5.5. Questions to Ask. 6. HOW TO DESIGN PRACTICAL ACCEPTANCE SAMPLING PLANS AND PROCESS VALIDATION STUDIES? 6.1. Single-Sample Attribute Plans. 6.2. Selecting AQL and RQL. 6.3. Other Acceptance Sampling Plans. 6.4. Designing Validation Studies. 6.5. Questions to Ask. 7. MANAGING AND IMPROVING PROCESSES: HOW TO USE AN AT-A-GLANCE-DISPLAY? 7.1. Statistical Logic of Control Limits. 7.2. Selecting Subgroup Size. 7.3. Selecting Sampling Interval. 7.4. Out-of-Control Rules. 7.5. Process Capability and Performance Indices. 7.6. At-A-Glance-Display. 7.7. Questions to Ask. 8. HOW TO FIND CAUSES OF VARIATION BY JUST LOOKING SYSTEMATICALLY? 8.1. Manufacturing Application. 8.2. Variance Components Analysis. 8.3. Planning for Quality Improvement. 8.4. Structured Studies. 8.5. Questions to Ask. 9. IS MY MEASUREMENT SYSTEM ACCEPTABLE AND HOW TO DESIGN, VALIDATE, AND IMPROVE IT? 9.1. Acceptance Criteria. 9.2. Designing Cost-Effective Sampling Schemes. 9.3. Designing a Robust Measurement System. 9.4. Measurement System Validation. 9.5. Repeatability and Reproducibility (R&R) Study. 9.6. Questions to Ask. 10. HOW TO USE THEORY EFFECTIVELY? 10.1. Empirical Models. 10.2. Mechanistic Models. 10.3. Mechanistic Model for Coat Weight CV. 10.4. Questions to Ask. 11. QUESTIONS AND ANSWERS. 11.1. Questions. 11.2. Answers. APPENDIX: TABLES. REFERENCES. INDEX.