05329nam 22006374a 450 991082982750332120230617030430.01-280-36769-597866103676960-470-24804-10-471-46537-20-471-72121-2(CKB)111087027111040(EBL)231738(OCoLC)56576084(SSID)ssj0000251090(PQKBManifestationID)11237319(PQKBTitleCode)TC0000251090(PQKBWorkID)10248046(PQKB)11121234(MiAaPQ)EBC231738(EXLCZ)9911108702711104020030224d2003 uy 0engur|n|---|||||txtccrStatistical methods for six sigma[electronic resource] in R&D and manufacturing /Anand M. JoglekarHoboken, NJ Wiley-Interscience20031 online resource (339 p.)Description based upon print version of record.0-471-20342-4 Includes bibliographical references (p. 317-318) and index.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 Intervals2.6 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 Charts4.3.1 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 Indices5.2.1 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 Charts6.4.2 Short-Run Average and Range ChartsA 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 learniQuality controlStatistical methodsProcess controlStatistical methodsQuality controlStatistical methods.Process controlStatistical methods.519.5551.51/5658.5/62Joglekar Anand M289808MiAaPQMiAaPQMiAaPQBOOK9910829827503321Statistical methods for six sigma754055UNINA03795nam 22005535 450 991038074860332120230329225557.03-030-41672-010.1007/978-3-030-41672-0(CKB)4100000010480271(DE-He213)978-3-030-41672-0(MiAaPQ)EBC6120872(PPN)24297922X(EXLCZ)99410000001048027120200220d2020 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierComplexity and Approximation In Memory of Ker-I Ko /edited by Ding-Zhu Du, Jie Wang1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (X, 289 p. 42 illus., 25 illus. in color.) Theoretical Computer Science and General Issues,2512-2029 ;12000Includes index.3-030-41671-2 In Memoriam: Ker-I Ko (1950-2018) -- Ker-I Ko and the Study of Resource-Bounded Kolmogorov Complexity -- The Power of Self-Reducibility Selectivity, Information, and Approximation -- Who Asked Us - How the Theory of Computing Answers, QuestionsAbout Analysis -- Promise Problems on Probability Distributions -- On Nonadaptive Reductions to the Set of Random Strings and its Dense Subsets -- Computability of the Solutions to Navier-Stokes Equations via Recursive Approximation -- Automatic Generation of Structured Overviews over a Very Large Corpus of Documents -- Better Upper Bounds for Searching on a Line with Byzantine Robots -- A Survey on Double Greedy Algorithms for Maximizing Non-monotone Submodular Functions -- Sequential Location Game on Continuous Directional Star Networks -- Core Decomposition, Maintenance and Applications -- Active and Busy Time Scheduling Problem: a Survey -- A Note on the Position Value for Hypergraph Communication Situations -- An Efficient Approximation Algorithm for the Steiner Tree Problem -- A Review for Submodular Optimization on Machine Scheduling Problems -- Edge Computing Integrated with Blockchain Technologies.This Festschrift is in honor of Ker-I Ko, Professor in the Stony Brook University, USA. Ker-I Ko was one of the founding fathers of computational complexity over real numbers and analysis. He and Harvey Friedman devised a theoretical model for real number computations by extending the computation of Turing machines. He contributed significantly to advancing the theory of structural complexity, especially on polynomial-time isomorphism, instance complexity, and relativization of polynomial-time hierarchy. Ker-I also made many contributions to approximation algorithm theory of combinatorial optimization problems. This volume contains 17 contributions in the area of complexity and approximation. Those articles are authored by researchers over the world, including North America, Europe and Asia. Most of them are co-authors, colleagues, friends, and students of Ker-I Ko.Theoretical Computer Science and General Issues,2512-2029 ;12000AlgorithmsMathematicsAlgorithmsApplications of MathematicsAlgorithms.Mathematics.Algorithms.Applications of Mathematics.005.1519.3Du Ding-Zhuedthttp://id.loc.gov/vocabulary/relators/edtWang Jieedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910380748603321Complexity and approximation245498UNINA