Editing Engineering statistics (section)

{{short description|Analysis of data by combining engineering and statistics}}
'''Engineering statistics''' combines [[engineering]] and [[statistics]] using [[scientific methods]] for analyzing data. Engineering statistics involves data concerning [[manufacturing]] processes such as: component dimensions, [[engineering tolerance|tolerances]], type of material, and fabrication process control. There are many methods used in engineering analysis and they are often displayed as [[histograms]] to give a visual of the data as opposed to being just numerical. Examples of methods are:<ref name="BHH"/><ref name="WuHamada"/><ref name="LogoWynn">{{cite book |author1=Logothetis, N. |author2=Wynn, H. P|title=Quality Through Design: Experimental Design, Off-line Quality Control, and Taguchi's Contributions
|publisher=Oxford U. P.
|year=1989 <!-- |pages=464+xi -->
|isbn=0-19-851993-1}}</ref><ref>Hogg, Robert V. and Ledolter, J. (1992). ''Applied Statistics for Engineers and Physical Scientists''. Macmillan, New York.</ref><ref>Walpole, Ronald; Myers, Raymond; Ye, Keying. ''Probability and Statistics for Engineers and Scientists''. Pearson Education, 2002, 7th edition, pg. 237</ref><ref name="Prentice Hall">{{cite book|last1=Rao|first1=Singiresu|title=Applied Numerical Methods of Engineers and Scientists|date=2002|publisher=Prentice Hall|location=Upper Saddle River, New Jersey|isbn=013089480X}}</ref>
# [[Design of Experiments]] (DOE) is a methodology for formulating scientific and engineering problems using statistical models. The protocol specifies a randomization procedure for the experiment and specifies the primary data-analysis, particularly in hypothesis testing. In a secondary analysis, the statistical analyst further examines the data to suggest other questions and to help plan future experiments. In engineering applications, the goal is often to optimize a process or product, rather than to subject a scientific hypothesis to test of its predictive adequacy.<ref name="BHH">[[George E. P. Box|Box, G. E.]], Hunter, W.G., Hunter, J.S., Hunter, W.G., "Statistics for Experimenters: Design, Innovation, and Discovery", 2nd Edition, Wiley, 2005, {{ISBN|0-471-71813-0}}</ref><ref name="WuHamada">{{cite book |author1=Wu, C. F. Jeff  |author2=Hamada, Michael |title=Experiments: Planning, Analysis, and Parameter Design Optimization |publisher=Wiley |year=2002 |isbn=0-471-25511-4}}</ref><ref name="LogoWynn"/> The use of [[optimal design|optimal (or near optimal) design]]s reduces the cost of experimentation.<ref name="WuHamada"/><ref name="ADT">{{cite book |author1=Atkinson, A. C. |author2=Donev, A. N. |author3=Tobias, R. D. |title=Optimum Experimental Designs, with SAS |url=https://books.google.com/books?id=oIHsrw6NBmoC|publisher=Oxford University Press |year=2007 |pages=511+xvi |isbn=978-0-19-929660-6 }}</ref>
# [[Quality control]] and [[process control]] use statistics as a tool to manage conformance to specifications of manufacturing processes and their products.<ref name="BHH"/><ref name="WuHamada"/><ref name="LogoWynn"/>
# [[Time and methods engineering]] use statistics to study repetitive operations in manufacturing in order to set standards and find optimum (in some sense) manufacturing procedures.
# [[Reliability engineering]] which measures the ability of a system to perform for its intended function (and time) and has tools for improving performance.<ref name="WuHamada"/><ref name="Barlow">{{cite book|last=Barlow|first=Richard E.|author-link=Richard E. Barlow|title=Engineering reliability|series=ASA-SIAM Series on Statistics and Applied Probability|publisher=Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA; American Statistical Association, Alexandria, VA|year=1998|pages=xx+199|isbn=0-89871-405-2|mr=1621421}}</ref><ref>Nelson, Wayne B., (2004), ''Accelerated Testing - Statistical Models, Test Plans, and Data Analysis'', John Wiley & Sons, New York, {{ISBN|0-471-69736-2}}</ref><ref>LogoWynn</ref>
# [[Probabilistic design]] involving the use of probability in product and system design
# [[System identification]] uses [[statistical method]]s to build [[mathematical model]]s of [[dynamical system]]s from measured data. System identification also includes the [[optimal design#System identification and stochastic approximation|optimal]] [[design of experiments]] for efficiently generating informative data for [[regression analysis|fitting]] such models.<ref>{{cite book |author1=Goodwin, Graham C.  |author2=Payne, Robert L.|title=Dynamic System Identification: Experiment Design and Data Analysis | publisher=Academic Press | year=1977 |isbn=0-12-289750-1}}</ref><ref>{{cite book |author1=Walter, Éric  |author2=Pronzato, Luc |title=Identification of Parametric Models from Experimental Data |publisher=Springer |year=1997}}
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