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Keller, Paul A.

Statistical Process Control Demystified (Kartoniert / Broschiert / Paperback)

McGraw-Hill Education Ltd, September 2011


320 S. - Sprache: Englisch - illustrations - 233x191x20 mm

ISBN: 0071742492 EAN: 9780071742498

Identify and reduce variation in business processes using SPC--the powerful analysis tool for process evaluation and improvement. Statistical Process Control Demystified shows you how to use SPC to enable data-driven decision making and gain a competitive advantage in the marketplace.Written in a step-by-step format, this practical guide explains how to analyze process data, collect data, and determine the suitability of a process in meeting requirements. Attribute and X-bar control charts are discussed, as are charts for individuals data. You'll also get details on process improvement and measurement systems analysis. Detailed examples, calculations, and statistical assumptions make it easy to understand the material, and end-of-chapter quizzes and a final exam help reinforce key concepts.It's a no-brainer! You'll learn about:Control chart interpretationOvercoming common errors in the use of SPC and general statistical analysis toolsSampling requirementsAnalysis using ExcelEstimating process variationDesigned experimentsMeasurement systems analysis, including R&R studiesContinuous process improvement strategiesSimple enough for a beginner, but challening enough for an advanced student, Statistical Process Control Demystified is your shortcut to this powerful analysis solution.


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Produktdetails

Statistical Process Control DeMYSTiFieD clearly defines the application and interpretation of SPC-a key component of Six Sigma, Quality Engineering, and Quality Assurance practices-within a cross section of industries. Various sampling methods applied in a variety of applications are discussed, including continuous, piece-part, service, and short run processes. The book uses Minitab, Excel and other software to demonstrate application of problem-solving and statistical techniques. You will learn how to solve complicated problems that are generally not addressed in introductory guides. High-level concepts are put it into practical context for ease of understanding. Statistical Process Control DeMYSTiFieD features: Chapter-opening objectives offering insight into what you're going to learn in each step Questions at the end of every chapter to reinforce learning and pinpoint weaknesses "Still Struggling?" icon providing specific recommendations for those having difficulty with certain subtopics A final exam for overall self-assessment "Curriculum Tree" that shows how SPC fits into a larger curriculum Hard stuff made easy! Analyzing process data; Definition of process; Choosing process metrics; Application of SPC to process analysis; Differences between SPC methods and the enumerative methods of statistical inference; Definition and interpretation of common causes and special causes of variation; Difference between statistical process control and process control using specification limits, including the effects of tampering; Misapplication of two-sigma control or warning limits and their implication on the false alarm rate; Establishing and revising control limits. Discussion of different applications of SPC across an organization; Statistical distributions; Types of data (variables vs. attributes); General properties of distributions; The Binomial Distribution; The Poisson Distribution; The Normal Distribution, including the Central Limit Theorem; Non-Normal Distributions; Detection using Normal Probability Plots, Anderson-Darling and K-S statistics. Standardization/translation techniques; Analysis of count data using attribute charts; Definition of attribute data; P charts and Np charts; Assumptions; Calculations; Interpretation, including examples. U charts and C charts; Limitations; The X-Bar Chart; Assumptions, including discussion of subgroup size selection, its influence on detection, and the relevance of rational subgroups; Calculations, including use of Run test rules; Interpretation, including examples; Limitations, including the effects of process autocorrelation, batch processes, multi-stream processes, measurement resolution; Charts for Individuals Data; The individual-X / moving range; Moving average charts; EWMA charts--assumptions; Calculations; Interpretation, including examples; Limitations; Process capability and performance analysis; Assumptions, including the need to establish statistical control, and the comparison of the use of process sigma versus sample standard deviation; Calculations, including for non-normal analysis; Interpretation, including examples; the relation to error rates, dpmo, sigma levels; and the proper reporting of capability indices. Limitations; Measurement systems analysis using control charts; Assumptions; Calculations; Interpretation, including examples; Limitations; Control charts for autocorrelated processes; Assumptions; Calculations; Interpretation, including examples; Limitations

Inhaltsverzeichnis

Chapter 1. Analyzing Process DataChapter 2. Data CollectionChapter 3. Determining Suitability to RequirementsChapter 4. Attribute Control ChartsChapter 5. X ChartsChapter 6. Charts for Individuals DataChapter 7. Process ImprovementChapter 8. Measurement Systems AnalysisChapter 9. Questions and Examples from PractitionersFinal ExamAnswers to Quizzes and Final ExamAppendix: Control Chart ConstantsReferencesIndex

Über den Autor

McGraw-Hill authors represent the leading experts in their fields and are dedicated to improving the lives, careers, and interests of readers worldwide


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