## 4 Discussion
They may be outliers or influencing data points in the literature. One research article states that a data point seriously influenced the coefficients' values, and the Cook's distance was very high [29]. In the study of [PERSON] et al. [30], the authors reported the results of checking influential data points with some criteria. The cause of the presence of influential data points could be experimental errors or the selection of the form of RSM models. Experimental errors may be due to sample preparation, instrument performance, or an operator's mistake. Different forms of regression equations, such as some nonlinear equations, could be used to improve the RSM model's fitting ability. Replicates of the experimental run at the same level could help the researcher to assess the significant difference between a data point and other data points under the same experimental conditions. Most datasets of experiments did not have replicates for each run; only one data point was available at each level and factor. This makes it difficult to justify the correctness of these data points further. Some research reports three replicates for each case [28; 31; 37; 39; 40; 43; 49]. However, only the mean of each run is used to perform the regression analysis. Through the use of the mean value instead of all replicates, the results of the regression coefficient values are the same, but the statistics of the statistical test are different. For example, there are 17 runs with three replicates for each run in the study of [PERSON] et al. [43]. As the mean of each run is used only, the sample size is 17. If all the original data are used, the sample number is increased to 51, and the degree of freedom for the residues is increased significantly. Then the statistical test of power could be improved significantly. The influencing points or outlierscould be justified if some influential data points were found. Three replicates for each run could provide some evidence for assessing the datasets. One advantage of RSM is the small number of experiments, which reduces the time and cost spent. However, a smaller number of samples becomes a disadvantage of the RSM method for evaluating an adequate equation. In comparing the required data numbers with some empirical equations (Equations (15)-(19)), the sample numbers of the RSM equation used in experiments were limited. In the experimental design, three replicates for each run could provide enough sample numbers to perform the regression analysis. Fifteen papers, 60% of the total literature, used Design Expert software. However, only one study [49] obtained an appropriate equation. [PERSON] et al. [34] used Minitab Ver. 14 software and proposed an adequate RSM equation. Based on the results of this study, some suggestions can be proposed for the utilization of RSM in experimental design:  
1. Training in the modern regression technique Receiving regression analysis training could enhance researchers' ability to propose adequate RSM equations
2. The backward elimination technique has been proven helpful for sequential variable selection. This method could be incorporated into commercial software to help researchers establish an adequate RSM equation. 3. Increasing the sample numbers to correspond to the minimum sample requirement is very important. This could enhance the power of the statistical test. Three replicates for one experiment run are recommended. All the data points with these replicates could be used to check the influential data point and decide whether it is an outlier or an influential point.