## 4 Discussion  
This study collected twenty-five datasets related to research to check the adequacy of RSM models. Only some papers reported an adequate equation to express the relationship between the response and influencing factors [34; 49]. All datasets are adopted from the literature. The original experimental data are listed in the studies. The common issues in the application of RSM in the literature are listed in Table 8.  
Most papers adopted a complete model and then plotted contour plots and 3D response surface plots to present the optimization of these variables. In the literature, \begin{table}
\begin{tabular}{l} \hline \hline
**Issue** \\ \hline
1. They adopted a complete model and then plotted contour and 3D response surface plots to present the optimization of these variables, without using the information of the coefficient value, t-value, and \(p\)-value for each variable in the ANOVA table. \\
2. The at-once variable deletion method was used to delete variables \\ whose \(p\)-value was higher than the preselected value (usually \(p<0.05\)). \\
3. Some variables were deleted with the at-once variable deletion method. However, the contour and response surface plots were produced with \\ the complete equations. \\
4. The ANOVA table of the sequential model was misused to keep all \\ variables in the linear or square term without significant testing for each \\ variable. \\
5. Datasets did not pass the normality test. \\
6. Datasets failed the constant variance test. \\
7. Influential data points were found. \\
8. There were three replicates for each run in the experiment. However, \\ only the mean of each run was used for the regression calculation. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The common issues in the application of RSM in the literature. the ANOVA tables included the coefficient value, t-value, and \(p\)-value for each variable. However, researchers did not use this information to screen the adequate equation [29; 30; 36; 37; 39; 40; 43; 44; 46; 47; 48; 49; 52]. Some papers used the at-once variable deletion method. After the first regression calculation, the ANOVA table of regression results showed each parameter's variables, coefficient values, standard error, t-value, and \(p\)-value. If the \(p\)-value of variables is higher than the preselected value (usually \(p<0.05\)), these variables are deleted simultaneously. The equation is then proposed using the first regression calculation's remaining variables and their coefficient values. This method is incorrect for model building [28; 51]. Some studies proposed the reported RSM equations using the at-once variable deletion method. However, they still produced the contour and response surface plots with the complete equations [31; 33; 35; 38; 45]. Section 2.5 introduced the misuse of the ANOVA table of the sequential-model sum of squares of the response. As the linear or square term significantly affects the response, all variables in the linear or square terms were accepted as affecting factors. This incorrect result was found in two studies [32; 42]. If the RSM models are inappropriate, the equations' contour plots and 3D response surface plots are incorrect, and the conduct of the optimization conditions of these variables is meaningless for researchers. For the twenty-five studies related to science and technology, some datasets did not pass the normality test [35; 40; 48; 50; 52], and some failed the constant variance test [28; 41; 51]. These datasets need to be transformed to correspond to the basic assumption of modern regression. The datasets of [PERSON] et al. [49] indicated the non-normal condition, and the authors used the logarithmic transformation to solve the problem. [PERSON] et al. [73] emphasized that the homoscedasticity assumption plays a more critical role than normality in the validity of ANOVA in checking the linear regression models. Departures from the homogeneous assumption will induce serious incorrect results [73]. Influential data points are usually found in the datasets of these studies.