東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences." ]

切換: 標籤 | MARC模式 | ISBD

Statistical methods for quality assu...

Vardeman, Stephen B.

FindBook

Google Book

Amazon

博客來

Statistical methods for quality assurance = basics, measurement, control, capability, and improvement /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical methods for quality assurance/ by Stephen B. Vardeman, J. Marcus Jobe.
其他題名:	basics, measurement, control, capability, and improvement /
作者:	Vardeman, Stephen B.
其他作者:	Jobe, J. Marcus.
出版者:	New York, NY :Springer New York : : 2016.,
面頁冊數:	xiv, 437 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Quality assurance - Statistical methods. -
電子資源:	http://dx.doi.org/10.1007/978-0-387-79106-7
ISBN:	9780387791067

Statistical methods for quality assurance = basics, measurement, control, capability, and improvement /
Vardeman, Stephen B.

Statistical methods for quality assurancebasics, measurement, control, capability, and improvement /[electronic resource] :by Stephen B. Vardeman, J. Marcus Jobe. - 2nd ed. - New York, NY :Springer New York :2016. - xiv, 437 p. :ill. (some col.), digital ;24 cm. - Springer texts in statistics,1431-875X. - Springer texts in statistics..

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.

ISBN: 9780387791067

Standard No.: 10.1007/978-0-387-79106-7doiSubjects--Topical Terms:

657693
Quality assurance
--Statistical methods.

LC Class. No.: TS156.6

Dewey Class. No.: 658.562

Statistical methods for quality assurance = basics, measurement, control, capability, and improvement /
LDR:03276nmm a2200349 a 4500 001 2048025
003 DE-He213
005 20170216154754.0
006 m d
007 cr nn 008maaau
008 170319s2016 nyu s 0 eng d
020 $a 9780387791067 $q (electronic bk.)
020 $a 9780387791050 $q (paper)
024 7 $a 10.1007/978-0-387-79106-7 $2 doi
035 $a 978-0-387-79106-7
040 $a GP $c GP
041 0 $a eng
050 4 $a TS156.6
072 7 $a PBT $2 bicssc
072 7 $a PD $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 658.562 $2 23
090 $a TS156.6 $b .V291 2016
100 1 $a Vardeman, Stephen B. $3 1641533
245 1 0 $a Statistical methods for quality assurance $h [electronic resource] : $b basics, measurement, control, capability, and improvement / $c by Stephen B. Vardeman, J. Marcus Jobe.
250 $a 2nd ed.
260 $a New York, NY : $b Springer New York : $b Imprint: Springer, $c 2016.
300 $a xiv, 437 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 1431-875X
505 0 $a 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.
520 $a 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.
650 0 $a Quality assurance $x Statistical methods. $3 657693
650 0 $a Industrial engineering $x Statistical methods. $3 714208
650 1 4 $a Statistics. $3 517247
650 2 4 $a Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1005896
700 1 $a Jobe, J. Marcus. $3 2210344
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer texts in statistics. $3 1567152
856 4 0 $u http://dx.doi.org/10.1007/978-0-387-79106-7
950 $a Mathematics and Statistics (Springer-11649)