Industrial statistics [electronic resource] : practical methods and guidance for improved performance / Anand M. Joglekar.

Joglekar, Anand M.
Oxford : Wileyl, c2010.
1 online resource (283 p.)

Location Notes Your Loan Policy


Process control -- Statistical methods.
Quality control -- Statistical methods.
Experimental design.
Electronic books.
HELPS YOU FULLY LEVERAGE STATISTICAL METHODS TO IMPROVE INDUSTRIAL PERFORMANCE Industrial Statistics guides you through ten practical statistical methods that have broad applications in many different industries for enhancing research, product design, process design, validation, manufacturing, and continuous improvement. As you progress through the book, you'll discover some valuable methods that are currently underutilized in industry as well as other methods that are often not used correctly. With twenty-five years of teaching and consulting experience, author Anand Jogleka
INDUSTRIAL STATISTICS; CONTENTS; 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
Description based upon print version of record.
Includes bibliographical references and index.