## 5 Conclusions  
This study collected twenty-five research datasets from the literature to evaluate their adequate RSM equations with modern regression analysis. The results of this study indicated some common issues in establishing RSM models. Most papers used a complete model to express the relationship between the response and the influential variables, producing contour and response surface plots. When researchers observe these plots to optimize these variables, the conclusions of the experiments may be incorrect. The ANOVA tables included the coefficient value, t-value, and \(p\)-value for each variable in the literature. However, researchers did not use this information to screen the important influencing variables. Some researchers used the at-once variable deletion method. That is, as the \(p\)-value of these variables is higher than the preselected value, these variables are deleted simultaneously. Some RSM equations were proposed using the at-once variable deletion method, and contour and response surface plots were produced with the complete equations. Some papers misused the sequential model of the ANOVA to accept all variables in linear or square terms, as these terms significantly affect the response. Actually, all variables need to be tested individually. Some datasets did not pass the normality test. Some datasets failed the constant variance test. Influential data points are found in most of the literature in this study.  
The suggestions for applying RSM for researchers are to enhance training in modern regression, use the back elimination method to evaluate the influencing variables in the RSM models, and increase the sample size with three replicates in each run. An adequate RSM model can optimize the influencing variables for response in science and technology.  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15137206/s1](https://www.mdpi.com/article/10.3390/app15137206/s1)]([https://www.mdpi.com/article/10.3390/app15137206/s1](https://www.mdpi.com/article/10.3390/app15137206/s1)), Supplement S1: The evaluation of the adequate equation for the TPC response [36]; Supplement S2: The evaluation of the adequate equation for the TPC response [36]; Supplement S3: The evaluation of the adequate equation for the response [45].  
**Author Contributions:** Conceptualization, H.-Y.C. and C.C.; methodology, H.-Y.C. and C.C.; software, C.C.; formal analysis, H.-Y.C.; investigation, H.-Y.C. and C.C.; data curation, H.-Y.C.; writing--original draft preparation, H.-Y.C. and C.C.; writing--review and editing, H.-Y.C. and C.C.; visualization, C.C. supervision, C.C.; project administration, C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors would like to thank the Ministry of Science and Technology of the Republic of China for financially supporting this research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.