The response surface methodology revisited – comparison of analytical and non-parametric approaches
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 no...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Printing House The Managers of Quality and Production Association
2018-09-01
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| Series: | Production Engineering Archives |
| Subjects: | |
| Online Access: | https://content.sciendo.com/view/journals/pea/20/20/article-p49.xml |
| Summary: | 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. |
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| ISSN: | 2353-5156 2353-7779 |