Editing Engineering statistics

{{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}}
</ref>

==History==

Engineering statistics dates back to 1000 B.C. when the [[Abacus]] was developed as means to calculate numerical data. In the 1600s, the development of information processing to systematically analyze and process data began. In 1654, the [[Slide Rule]] technique was developed by [https://www.britannica.com/science/slide-rule#ref81001 Robert Bissaker] for advanced data calculations. In 1833, a British mathematician named [[Charles Babbage]] designed the idea of an automatic computer which inspired developers at [[Harvard University]] and [[IBM]] to design the first mechanical automatic-sequence-controlled calculator called [[harvard mark I|MARK I]]. The integration of computers and calculators into the industry brought about a more efficient means of analyzing data and the beginning of engineering statistics.<ref>{{cite web|last1=The Editors of Encyclopaedia Britannica|title=Slide Rule|url=https://www.britannica.com/science/slide-rule#ref81001|website=Encyclopaedia Britannica|publisher=Encyclopaedia Britannica Inc|access-date=17 April 2018}}</ref><ref name="Prentice Hall"/><ref>{{cite book|last1=Montgomery|first1=Douglas|last2=Runger|first2=George|last3=Hubele|first3=Norma|title=Engineering Statistics|date=21 December 2010 |isbn=978-0470631478|edition=5}}</ref>

== Examples ==

=== Factorial Experimental Design ===
{{Main|Factorial experiment}}
A factorial experiment is one where, contrary to the standard experimental philosophy of changing only one independent variable and holding everything else constant, multiple independent variables are tested at the same time. With this design, statistical engineers can see both the direct effects of one independent variable ([[main effect]]), as well as potential [[Interaction (statistics)|interaction effects]] that arise when multiple independent variables provide a different result when together than either would on its own.

=== Six Sigma ===
{{Main|Six Sigma}}
Six Sigma is a set of techniques to improve the reliability of a manufacturing process. Ideally, all products will have the exact same specifications equivalent to what was desired, but countless imperfections of real-world manufacturing makes this impossible. The as-built specifications of a product are assumed to be centered around a mean, with each individual product deviating some amount away from that mean in a normal distribution. The goal of Six Sigma is to ensure that the acceptable specification limits are six [[standard deviation]]s away from the mean of the distribution; in other words, that each step of the manufacturing process has at most a 0.00034% chance of producing a defect.

==Notes==
<references/>

==References==
*{{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 }}
*[[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}}
*{{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}}
*{{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}}
*{{cite web|author=((The Editors of Encyclopaedia Britannica))|title=Slide Rule|url=https://www.britannica.com/science/slide-rule#ref81001|website=Encyclopaedia Britannica|publisher=Encyclopaedia Britannica Inc|access-date=17 April 2018}}
*{{cite book|last1=Montgomery|first1=Douglas|last2=Runger|first2=George|last3=Hubele|first3=Norma|title=Engineering Statistics|date=21 December 2010 |isbn=978-0470631478|edition=5}}

==External links==
*{{Commons category-inline}}

{{Statistics|applications|state=expanded}}

{{DEFAULTSORT:Engineering Statistics}}
[[Category:Engineering statistics| ]]