<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Since G.E.P. Box introduced central composite designs in early fifties of 20th century, the classic design of experiments (DoE) utilizes response surface models (RSM), however usually limited to the simple form of low-degree polynomials. In the case of small size datasets, the conformity with the normal distribution has very weak reliability and it leads to very uncertain assessment of a parameter statistical significance. The bootstrap approach appears to be better solution than - theoretically proved but only asymptotically equal - t distribution based evaluation. The authors presents the comparison of the RSM model evaluated by a classic method and bootstrap approach.</p></abstract>

Since G.E.P. Box introduced central composite designs in early fifties of 20th century, the classic design of experiments (DoE) utilizes response surface models (RSM), however usually limited to the simple form of low-degree polynomials. In the case of small size datasets, the conformity with the normal distribution has very weak reliability and it leads to very uncertain assessment of a parameter statistical significance. The bootstrap approach appears to be better solution than - theoretically proved but only asymptotically equal - t distribution based evaluation. The authors presents the comparison of the RSM model evaluated by a classic method and bootstrap approach.

The Response Surface Methodology revisited – comparison of analytical and non-parametric approaches

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This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Since G.E.P. Box introduced central composite designs in early fifties of 20th century, the classic design of experiments (DoE) utilizes response surface...

The Response Surface Methodology revisited – comparison of analytical and non-parametric approaches

Online veröffentlicht: 16. Nov. 2018

Seitenbereich: 49 - 53

Eingereicht: 10. Juli 2018

Akzeptiert: 24. Sept. 2018

DOI: https://doi.org/10.30657/pea.2018.20.10

Schlüsselwörter
expert system, design of experiment, factorials, Taguchi robust design, RSM

© 2018 Przemysław Osocha, Jordan Podgórski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The Response Surface Methodology revisited – comparison of analytical and non-parametric approaches

Online veröffentlicht: 16. Nov. 2018

Seitenbereich: 49 - 53

Eingereicht: 10. Juli 2018

Akzeptiert: 24. Sept. 2018

DOI: https://doi.org/10.30657/pea.2018.20.10

Schlüsselwörterexpert system, design of experiment, factorials, Taguchi robust design, RSM

© 2018 Przemysław Osocha, Jordan Podgórski, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
expert system, design of experiment, factorials, Taguchi robust design, RSM