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Record Nr. |
UNINA9910143182603321 |
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Autore |
Joglekar Anand M |
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Titolo |
Statistical methods for six sigma [[electronic resource] ] : in R&D and manufacturing / / Anand M. Joglekar |
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Pubbl/distr/stampa |
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Hoboken, NJ, : Wiley-Interscience, 2003 |
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ISBN |
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1-280-36769-5 |
9786610367696 |
0-470-24804-1 |
0-471-46537-2 |
0-471-72121-2 |
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Descrizione fisica |
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1 online resource (339 p.) |
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Disciplina |
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Soggetti |
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Quality control - Statistical methods |
Process control - Statistical methods |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. 317-318) and index. |
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Nota di contenuto |
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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 |
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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 |
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Sommario/riassunto |
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A guide to achieving business successes through statistical methods Statistical methods are a key ingredient in providing data-based guidance to research and development as well as to manufacturing. Understanding the concepts and specific steps involved in each statistical method is critical for achieving consistent and on-target performance. Written by a recognized educator in the field, Statistical Methods for Six Sigma: In R&D and Manufacturing is specifically geared to engineers, scientists, technical managers, and other technical professionals in industry. Emphasizing practical learni |
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