## 3 Results
### Three Variables
#### 3.2.4 The Adequate Equations of the RSM in the Other Literature  
Table 6 lists the results of evaluating the adequate RSM equations for the three variables studied based on the literature. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Purpose**} & **Reported** & **Contour and** & **Acquate** & **Normality Test** & **Constant** & **Influential Data** \\  & & **Equations** & **Surface Plots** & **Equations** & **Normality Test** & **Variance Test** & **Influential Data** \\ \hline \multirow{8}{*}{[PERSON] et.al. [34]} & CO\({}_{2}\) extraction of & & & & & & \\  & bioactive & flavonoid & y\({}_{2}\)k extract ratio = & & & & & \\  & compounds & complete & equation & & & & \\  & x\({}_{1}\): temperature & equation & & & &  
The RSM model reported by [PERSON] et al. [34] was a complete equation. The modern regression results are the same. Two influential data points were found, which required further study. Two responses were studied for the enzymatic clarification of green asparagus juice [35]. With the ANOVA tables, the variables with a \(p\)-value > 0.05 were deleted at the same time in this study. Their RSM equations were proposed with the remaining variables and estimated values. After checking using modern regression, the \(y_{\text{Carity}}\) response had the same form as the RSM equations and the \(y_{\text{DPPH}}\) was different [35]. [PERSON] et al. [38] reported the aqueous extraction of virgin coconut oil. The affecting factors were screened with the \(p\)-values in their ANOVA tables, which were significant (\(p\leq 0.05\)). The RSM equations were then established with the remaining variables. In the study [38], only the proposed equations of the \(y_{\text{pov}}\) response have the same results as our study. [PERSON] et al. [39] investigated four physicochemical properties of a pumpkin flour blend with corn. The RSM equations were not reported in the study. The contour and response surface plots showed that all variables had a curvilinear effect on the response. However, the results of modern regression in Table 6 indicated that the variables of the adequate equations of the four responses were not complete models. The influence factors for \(y_{\text{TER}}\) (radial expansion ratio) were \(x_{1}\), \(x_{3}\), and \(x_{1}x_{2}\); no quadratic relationship existed. The affecting factors for the \(y_{\text{HD}}\) response (hardness) were \(x_{1}\), \(x_{2}\), \(x_{3}\), and \(x_{1}{}^{2}\). Only the \(x_{1}\) factor had a curvilinear relationship with \(y_{\text{HD}}\). The coefficients of variables and a significant test with \(p\)-values were presented in a literature table. However, the authors did not use this information to select adequate models [39]. [PERSON] et al. [40] used RSM to evaluate the effects of extrusion variables and maleic anhydride content on biopolymer blends. They used the complete models to describe the RSM equations and to present the curve distribution of the contour plot and response surface plots. Regression results of a significant effect of variables on response have been reported in the study [40]. However, these statistical results were not used to assess whether the variables had a significant effect. All variables were used as the affecting factors of the response. For the \(y_{\text{TS}}\) response, only \(x_{3}\) has quadratic terms. The quadratic terms are \(x_{2}{}^{2}\) and \(x_{3}{}^{2}\) for \(y_{\text{EL}}\), and \(x_{1}{}^{2}\) and \(x_{2}{}^{2}\) for \(y_{\text{WA}}\). The complete model involved all factors in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Purpose**} & **Reported** & **Contour and** & **Acquate** & **Normality Test** & **Constant** & **Influential Data** \\  & & **Equations** & **Surface Plots** & **Equations** & **Variance Test** & **Influential Data** \\ \hline \multirow{4}{*}{ \begin{tabular}{c} [PERSON] et.al. \\ [42] \\ \end{tabular}