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Industrial statistics [[electronic resource] ] : practical methods and guidance for improved performance / / Anand M. Joglekar
Industrial statistics [[electronic resource] ] : practical methods and guidance for improved performance / / Anand M. Joglekar
Autore Joglekar Anand M
Pubbl/distr/stampa Oxford, : Wileyl, c2010
Descrizione fisica 1 online resource (283 p.)
Disciplina 658.500727
Soggetto topico Process control - Statistical methods
Quality control - Statistical methods
Experimental design
ISBN 1-282-68662-3
9786612686627
0-470-58414-9
0-470-58412-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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
Record Nr. UNISA-996215538503316
Joglekar Anand M  
Oxford, : Wileyl, c2010
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Industrial statistics [[electronic resource] ] : practical methods and guidance for improved performance / / Anand M. Joglekar
Industrial statistics [[electronic resource] ] : practical methods and guidance for improved performance / / Anand M. Joglekar
Autore Joglekar Anand M
Pubbl/distr/stampa Oxford, : Wileyl, c2010
Descrizione fisica 1 online resource (283 p.)
Disciplina 658.500727
Soggetto topico Process control - Statistical methods
Quality control - Statistical methods
Experimental design
ISBN 1-282-68662-3
9786612686627
0-470-58414-9
0-470-58412-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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
Record Nr. UNINA-9910140598003321
Joglekar Anand M  
Oxford, : Wileyl, c2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Industrial statistics [[electronic resource] ] : practical methods and guidance for improved performance / / Anand M. Joglekar
Industrial statistics [[electronic resource] ] : practical methods and guidance for improved performance / / Anand M. Joglekar
Autore Joglekar Anand M
Pubbl/distr/stampa Oxford, : Wileyl, c2010
Descrizione fisica 1 online resource (283 p.)
Disciplina 658.500727
Soggetto topico Process control - Statistical methods
Quality control - Statistical methods
Experimental design
ISBN 1-282-68662-3
9786612686627
0-470-58414-9
0-470-58412-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 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
Record Nr. UNINA-9910809710903321
Joglekar Anand M  
Oxford, : Wileyl, c2010
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical methods for six sigma [[electronic resource] ] : in R&D and manufacturing / / Anand M. Joglekar
Statistical methods for six sigma [[electronic resource] ] : in R&D and manufacturing / / Anand M. Joglekar
Autore Joglekar Anand M
Pubbl/distr/stampa Hoboken, NJ, : Wiley-Interscience, 2003
Descrizione fisica 1 online resource (339 p.)
Disciplina 519.5
551.51/5
658.5/62
Soggetto topico Quality control - Statistical methods
Process control - Statistical methods
Soggetto genere / forma Electronic books.
ISBN 1-280-36769-5
9786610367696
0-470-24804-1
0-471-46537-2
0-471-72121-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Statistical Methods for Six Sigma; Contents; Preface; 1 Introduction; 2 Basic Statistics; 2.1 Descriptive Statistics; 2.1.1 Measures of Central Tendency; 2.1.2 Measures of Variability; 2.1.3 Histogram; 2.2 Statistical Distributions; 2.2.1 Normal Distribution; 2.2.2 Binomial Distribution; 2.2.3 Poisson Distribution; 2.3 Confidence Intervals; 2.3.1 Confidence Interval for m; 2.3.2 Confidence Interval for s; 2.3.3 Confidence Interval for p and l; 2.4 Sample Size; 2.4.1 Sample Size to Estimate m; 2.4.2 Sample Size to Estimate s; 2.4.3 Sample Size to Estimate p and l; 2.5 Tolerance Intervals
2.6 Normality, Independence, and Homoscedasticity2.6.1 Normality; 2.6.2 Independence; 2.6.3 Homoscedasticity; 3 Comparative Experiments and Regression Analysis; 3.1 Hypothesis Testing Framework; 3.2 Comparing Single Population; 3.2.1 Comparing Mean (Variance Known); 3.2.2 Comparing Mean (Variance Unknown); 3.2.3 Comparing Standard Deviation; 3.2.4 Comparing Proportion; 3.3 Comparing Two Populations; 3.3.1 Comparing Two Means (Variance Known); 3.3.2 Comparing Two Means (Variance Unknown but Equal); 3.3.3 Comparing Two Means (Variance Unknown and Unequal)
3.3.4 Comparing Two Means (Paired t-test)3.3.5 Comparing Two Standard Deviations; 3.3.6 Comparing Two Proportions; 3.4 Comparing Multiple Populations; 3.4.1 Completely Randomized Design; 3.4.2 Randomized Block Design; 3.4.3 Multiple Comparison Procedures; 3.4.4 Comparing Multiple Standard Deviations; 3.5 Correlation; 3.5.1 Scatter Diagram; 3.5.2 Correlation Coefficient; 3.6 Regression Analysis; 3.6.1 Fitting Equations to Data; 3.6.2 Accelerated Stability Tests; 4 Control Charts; 4.1 Role of Control Charts; 4.2 Logic of Control Limits; 4.3 Variable Control Charts
4.3.1 Average and Range Charts4.3.2 Average and Standard Deviation Charts; 4.3.3 Individual and Moving Range Charts; 4.4 Attribute Control Charts; 4.4.1 Fraction Defective (p) Chart; 4.4.2 Defects per Product (u) Chart; 4.5 Interpreting Control Charts; 4.5.1 Tests for the Chart of Averages; 4.5.2 Tests for Other Charts; 4.6 Key Success Factors; 5 Process Capability; 5.1 Capability and Performance Indices; 5.1.1 C(p) Index; 5.1.2 C(pk) Index; 5.1.3 P(p) Index; 5.1.4 P(pk) Index; 5.1.5 Relationships between C(p), C(pk), P(p), and P(pk); 5.2 Estimating Capability and Performance Indices
5.2.1 Point Estimates for Capability and Performance Indices5.2.2 Confidence Intervals for Capability and Performance Indices; 5.2.3 Connection with Tolerance Intervals; 5.3 Six-Sigma Goal; 5.4 Planning for Improvement; 6 Other Useful Charts; 6.1 Risk-based Control Charts; 6.1.1 Control Limits, Subgroup Size, and Risks; 6.1.2 Risk-Based X Chart; 6.1.3 Risk-Based Attribute Charts; 6.2 Modified Control Limit X Chart; 6.2.1 Chart Design; 6.2.2 Required Minimum C(pk); 6.3 Moving Average Control Chart; 6.4 Short-Run Control Charts; 6.4.1 Short-Run Individual and Moving Range Charts
6.4.2 Short-Run Average and Range Charts
Record Nr. UNINA-9910143182603321
Joglekar Anand M  
Hoboken, NJ, : Wiley-Interscience, 2003
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical methods for six sigma [[electronic resource] ] : in R&D and manufacturing / / Anand M. Joglekar
Statistical methods for six sigma [[electronic resource] ] : in R&D and manufacturing / / Anand M. Joglekar
Autore Joglekar Anand M
Pubbl/distr/stampa Hoboken, NJ, : Wiley-Interscience, 2003
Descrizione fisica 1 online resource (339 p.)
Disciplina 519.5
551.51/5
658.5/62
Soggetto topico Quality control - Statistical methods
Process control - Statistical methods
ISBN 1-280-36769-5
9786610367696
0-470-24804-1
0-471-46537-2
0-471-72121-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Statistical Methods for Six Sigma; Contents; Preface; 1 Introduction; 2 Basic Statistics; 2.1 Descriptive Statistics; 2.1.1 Measures of Central Tendency; 2.1.2 Measures of Variability; 2.1.3 Histogram; 2.2 Statistical Distributions; 2.2.1 Normal Distribution; 2.2.2 Binomial Distribution; 2.2.3 Poisson Distribution; 2.3 Confidence Intervals; 2.3.1 Confidence Interval for m; 2.3.2 Confidence Interval for s; 2.3.3 Confidence Interval for p and l; 2.4 Sample Size; 2.4.1 Sample Size to Estimate m; 2.4.2 Sample Size to Estimate s; 2.4.3 Sample Size to Estimate p and l; 2.5 Tolerance Intervals
2.6 Normality, Independence, and Homoscedasticity2.6.1 Normality; 2.6.2 Independence; 2.6.3 Homoscedasticity; 3 Comparative Experiments and Regression Analysis; 3.1 Hypothesis Testing Framework; 3.2 Comparing Single Population; 3.2.1 Comparing Mean (Variance Known); 3.2.2 Comparing Mean (Variance Unknown); 3.2.3 Comparing Standard Deviation; 3.2.4 Comparing Proportion; 3.3 Comparing Two Populations; 3.3.1 Comparing Two Means (Variance Known); 3.3.2 Comparing Two Means (Variance Unknown but Equal); 3.3.3 Comparing Two Means (Variance Unknown and Unequal)
3.3.4 Comparing Two Means (Paired t-test)3.3.5 Comparing Two Standard Deviations; 3.3.6 Comparing Two Proportions; 3.4 Comparing Multiple Populations; 3.4.1 Completely Randomized Design; 3.4.2 Randomized Block Design; 3.4.3 Multiple Comparison Procedures; 3.4.4 Comparing Multiple Standard Deviations; 3.5 Correlation; 3.5.1 Scatter Diagram; 3.5.2 Correlation Coefficient; 3.6 Regression Analysis; 3.6.1 Fitting Equations to Data; 3.6.2 Accelerated Stability Tests; 4 Control Charts; 4.1 Role of Control Charts; 4.2 Logic of Control Limits; 4.3 Variable Control Charts
4.3.1 Average and Range Charts4.3.2 Average and Standard Deviation Charts; 4.3.3 Individual and Moving Range Charts; 4.4 Attribute Control Charts; 4.4.1 Fraction Defective (p) Chart; 4.4.2 Defects per Product (u) Chart; 4.5 Interpreting Control Charts; 4.5.1 Tests for the Chart of Averages; 4.5.2 Tests for Other Charts; 4.6 Key Success Factors; 5 Process Capability; 5.1 Capability and Performance Indices; 5.1.1 C(p) Index; 5.1.2 C(pk) Index; 5.1.3 P(p) Index; 5.1.4 P(pk) Index; 5.1.5 Relationships between C(p), C(pk), P(p), and P(pk); 5.2 Estimating Capability and Performance Indices
5.2.1 Point Estimates for Capability and Performance Indices5.2.2 Confidence Intervals for Capability and Performance Indices; 5.2.3 Connection with Tolerance Intervals; 5.3 Six-Sigma Goal; 5.4 Planning for Improvement; 6 Other Useful Charts; 6.1 Risk-based Control Charts; 6.1.1 Control Limits, Subgroup Size, and Risks; 6.1.2 Risk-Based X Chart; 6.1.3 Risk-Based Attribute Charts; 6.2 Modified Control Limit X Chart; 6.2.1 Chart Design; 6.2.2 Required Minimum C(pk); 6.3 Moving Average Control Chart; 6.4 Short-Run Control Charts; 6.4.1 Short-Run Individual and Moving Range Charts
6.4.2 Short-Run Average and Range Charts
Record Nr. UNINA-9910829827503321
Joglekar Anand M  
Hoboken, NJ, : Wiley-Interscience, 2003
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Statistical methods for six sigma [[electronic resource] ] : in R&D and manufacturing / / Anand M. Joglekar
Statistical methods for six sigma [[electronic resource] ] : in R&D and manufacturing / / Anand M. Joglekar
Autore Joglekar Anand M
Pubbl/distr/stampa Hoboken, NJ, : Wiley-Interscience, 2003
Descrizione fisica 1 online resource (339 p.)
Disciplina 519.5
551.51/5
658.5/62
Soggetto topico Quality control - Statistical methods
Process control - Statistical methods
ISBN 1-280-36769-5
9786610367696
0-470-24804-1
0-471-46537-2
0-471-72121-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Statistical Methods for Six Sigma; Contents; Preface; 1 Introduction; 2 Basic Statistics; 2.1 Descriptive Statistics; 2.1.1 Measures of Central Tendency; 2.1.2 Measures of Variability; 2.1.3 Histogram; 2.2 Statistical Distributions; 2.2.1 Normal Distribution; 2.2.2 Binomial Distribution; 2.2.3 Poisson Distribution; 2.3 Confidence Intervals; 2.3.1 Confidence Interval for m; 2.3.2 Confidence Interval for s; 2.3.3 Confidence Interval for p and l; 2.4 Sample Size; 2.4.1 Sample Size to Estimate m; 2.4.2 Sample Size to Estimate s; 2.4.3 Sample Size to Estimate p and l; 2.5 Tolerance Intervals
2.6 Normality, Independence, and Homoscedasticity2.6.1 Normality; 2.6.2 Independence; 2.6.3 Homoscedasticity; 3 Comparative Experiments and Regression Analysis; 3.1 Hypothesis Testing Framework; 3.2 Comparing Single Population; 3.2.1 Comparing Mean (Variance Known); 3.2.2 Comparing Mean (Variance Unknown); 3.2.3 Comparing Standard Deviation; 3.2.4 Comparing Proportion; 3.3 Comparing Two Populations; 3.3.1 Comparing Two Means (Variance Known); 3.3.2 Comparing Two Means (Variance Unknown but Equal); 3.3.3 Comparing Two Means (Variance Unknown and Unequal)
3.3.4 Comparing Two Means (Paired t-test)3.3.5 Comparing Two Standard Deviations; 3.3.6 Comparing Two Proportions; 3.4 Comparing Multiple Populations; 3.4.1 Completely Randomized Design; 3.4.2 Randomized Block Design; 3.4.3 Multiple Comparison Procedures; 3.4.4 Comparing Multiple Standard Deviations; 3.5 Correlation; 3.5.1 Scatter Diagram; 3.5.2 Correlation Coefficient; 3.6 Regression Analysis; 3.6.1 Fitting Equations to Data; 3.6.2 Accelerated Stability Tests; 4 Control Charts; 4.1 Role of Control Charts; 4.2 Logic of Control Limits; 4.3 Variable Control Charts
4.3.1 Average and Range Charts4.3.2 Average and Standard Deviation Charts; 4.3.3 Individual and Moving Range Charts; 4.4 Attribute Control Charts; 4.4.1 Fraction Defective (p) Chart; 4.4.2 Defects per Product (u) Chart; 4.5 Interpreting Control Charts; 4.5.1 Tests for the Chart of Averages; 4.5.2 Tests for Other Charts; 4.6 Key Success Factors; 5 Process Capability; 5.1 Capability and Performance Indices; 5.1.1 C(p) Index; 5.1.2 C(pk) Index; 5.1.3 P(p) Index; 5.1.4 P(pk) Index; 5.1.5 Relationships between C(p), C(pk), P(p), and P(pk); 5.2 Estimating Capability and Performance Indices
5.2.1 Point Estimates for Capability and Performance Indices5.2.2 Confidence Intervals for Capability and Performance Indices; 5.2.3 Connection with Tolerance Intervals; 5.3 Six-Sigma Goal; 5.4 Planning for Improvement; 6 Other Useful Charts; 6.1 Risk-based Control Charts; 6.1.1 Control Limits, Subgroup Size, and Risks; 6.1.2 Risk-Based X Chart; 6.1.3 Risk-Based Attribute Charts; 6.2 Modified Control Limit X Chart; 6.2.1 Chart Design; 6.2.2 Required Minimum C(pk); 6.3 Moving Average Control Chart; 6.4 Short-Run Control Charts; 6.4.1 Short-Run Individual and Moving Range Charts
6.4.2 Short-Run Average and Range Charts
Record Nr. UNINA-9910841408003321
Joglekar Anand M  
Hoboken, NJ, : Wiley-Interscience, 2003
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui