LEADER 05182nam 2200649Ia 450 001 996215538503316 005 20230725023517.0 010 $a1-282-68662-3 010 $a9786612686627 010 $a0-470-58414-9 010 $a0-470-58412-2 035 $a(CKB)2670000000019299 035 $a(EBL)533961 035 $a(OCoLC)630544534 035 $a(SSID)ssj0000420504 035 $a(PQKBManifestationID)11295568 035 $a(PQKBTitleCode)TC0000420504 035 $a(PQKBWorkID)10392382 035 $a(PQKB)11606149 035 $a(MiAaPQ)EBC533961 035 $a(Au-PeEL)EBL533961 035 $a(CaPaEBR)ebr10388301 035 $a(CaONFJC)MIL268662 035 $a(EXLCZ)992670000000019299 100 $a20090824d2010 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIndustrial statistics$b[electronic resource] $epractical methods and guidance for improved performance /$fAnand M. Joglekar 210 $aOxford $cWileyl$dc2010 215 $a1 online resource (283 p.) 300 $aDescription based upon print version of record. 311 $a0-470-49716-5 320 $aIncludes bibliographical references and index. 327 $aINDUSTRIAL 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 327 $a2.7. 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 327 $a4.7. 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 327 $a7.2. 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 327 $a9.3. 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 330 $aHELPS 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 606 $aProcess control$xStatistical methods 606 $aQuality control$xStatistical methods 606 $aExperimental design 615 0$aProcess control$xStatistical methods. 615 0$aQuality control$xStatistical methods. 615 0$aExperimental design. 676 $a658.500727 700 $aJoglekar$b Anand M$0289808 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996215538503316 996 $aIndustrial statistics$91987155 997 $aUNISA