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＜電子ブック＞
Statistical Methods for Quality Assurance : Basics, Measurement, Control, Capability, and Improvement / by Stephen B. Vardeman, J. Marcus Jobe
(Springer Texts in Statistics. ISSN:21974136)

版	2nd ed. 2016.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2016
大きさ	XIV, 437 p. 104 illus., 99 illus. in color : online resource
著者標目	*Vardeman, Stephen B author Jobe, J. Marcus author SpringerLink (Online service)
件　名	LCSH:Statistics  FREE:Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences
一般注記	Introduction -- Statistics and Measurement -- Process Monitoring -- Process Characterization and Capability Analysis -- Experiment Design and Analysis for Process Improvement Part 1: Basics -- Experiment Design and Analysis for Process Improvement Part 2: Advanced Topics -- A Tables This undergraduate statistical quality assurance textbook clearly shows with real projects, cases and data sets how statistical quality control tools are used in practice.  Among the topics covered is a practical evaluation of measurement effectiveness for both continuous and discrete data.  Gauge Reproducibility and Repeatability methodology (including confidence intervals for Repeatability, Reproducibility and the Gauge Capability Ratio) is thoroughly developed. Process capability indices and corresponding confidence intervals are also explained.  In addition to process monitoring techniques, experimental design and analysis for process improvement are carefully presented. Factorial and Fractional Factorial arrangements of treatments and Response Surface methods are covered. Integrated throughout the book are rich sets of examples and problems that help readers gain a better understanding of where and how to apply statistical quality control tools.  These large and realistic problem sets in combination with the streamlined approach of the text and extensive supporting material facilitate reader understanding. Second Edition Improvements Extensive coverage of measurement quality evaluation (in addition to ANOVA Gauge R&R methodologies) New end-of-section exercises and revised-end-of-chapter exercises Two full sets of slides, one with audio to assist student preparation outside-of-class and another appropriate for professors’ lectures Substantial supporting material Supporting Material Seven R programs that support variables and attributes control chart construction and analyses, Gauge R&R methods, analyses of Fractional Factorial studies,  Propagation of Error analyses and Response Surface analyses Documentation for the R programs Excel data files associated with the end-of-chapter problem sets, most from real engineering settings HTTP:URL=https://doi.org/10.1007/978-0-387-79106-7

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