Importance of Using Modern Regression Analysis for Response Surface Models in Science and Technology
Experimental design is important for researchers and those in other fields to find factors affecting an experimental response. The response surface methodology (RSM) is a special experimental design used to evaluate the significant factors influencing a process and confirm the optimum conditions for...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/13/7206 |
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| Summary: | Experimental design is important for researchers and those in other fields to find factors affecting an experimental response. The response surface methodology (RSM) is a special experimental design used to evaluate the significant factors influencing a process and confirm the optimum conditions for different factors. RSM models represent the relationship between the response and the influencing factors established with the regression analysis. Then these equations are used to produce the contour and response surface plots for observers to determine the optimization. The influence of regression techniques on model building has not been thoroughly studied. This study collected twenty-five datasets from the literature. The backward elimination procedure and <i>t</i>-test value of each variable were adopted to evaluate the significant effect on the response. Modern regression techniques were used. The results of this study present some problems of RSM studies in the previous literature, including using the complete equation without checking the statistical test, using the at-once variable deletion method to delete the variables whose <i>p</i>-values are higher than the preset value, the inconsistency between the proposed RSM equations and the contour and response surface plots, the misuse of the ANOVA table of the sequential model to keep all variables in the linear or square term without testing for each variable, the non-normal and non-constant variance conditions of datasets, and the finding of some influential data points. The suggestions for applying RSM for researchers are training in the modern regression technique, using the backward elimination technique for sequential variable selection, and increasing the sample numbers with three replicates for each run. |
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| ISSN: | 2076-3417 |