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# Engineering statistics

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### Engineering statistics

Engineering statistics combines engineering and statistics:[1][2][3][4][5]

1. 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.[1][2][3] The use of optimal (or near optimal) designs reduces the cost of experimentation.[2][6]
2. Quality control and process control use statistics as a tool to manage conformance to specifications of manufacturing processes and their products.[1][2][3]
3. 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.
4. Reliability engineering which measures the ability of a system to perform for its intended function (and time) and has tools for improving performance.[2][7][8][9]
5. Probabilistic design involving the use of probability in product and system design
6. System identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models.[10][11]

## Notes

1. ^ a b c 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
2. ^ a b c d e Wu, C. F. Jeff and Hamada, Michael (2002). Experiments: Planning, Analysis, and Parameter Design Optimization. Wiley.
3. ^ a b c Logothetis, N. and Wynn, H. P (1989). Quality Through Design: Experimental Design, Off-line Quality Control, and Taguchi's Contributions. Oxford U. P.
4. ^ Hogg, Robert V. and Ledolter, J. (1992). Applied Statistics for Engineers and Physical Scientists. Macmillan, New York.
5. ^ Walpole, Ronald; Myers, Raymond; Ye, Keying. Probability and Statistics for Engineers and Scientists. Pearson Education, 2002, 7th edition, pg. 237
6. ^ Atkinson, A. C. and Donev, A. N. and Tobias, R. D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. pp. 511+xvi.
7. ^
8. ^ Nelson, Wayne B., (2004), Accelerated Testing - Statistical Models, Test Plans, and Data Analysis, John Wiley & Sons, New York, ISBN 0-471-69736-2
9. ^ LogoWynn
10. ^ Goodwin, Graham C. and Payne, Robert L. (1977). Dynamic System Identification: Experiment Design and Data Analysis. Academic Press.
11. ^ Walter, Éric and Pronzato, Luc (1997). Identification of Parametric Models from Experimental Data. Springer.

## References

• Atkinson, A. C. and Donev, A. N. and Tobias, R. D. (2007). Optimum Experimental Designs, with SAS. Oxford University Press. pp. 511+xvi.
• 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
• Logothetis, N. and Wynn, H. P (1989). Quality Through Design: Experimental Design, Off-line Quality Control, and Taguchi's Contributions. Oxford U. P.
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