# Optimizing the Sodium Hydroxide Conversion Using Regression Analysis in CSTR
## 3 Results and Discussion
### Experimental Findings
The regression analysis was examined to obtain optimum NaOH conversion \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multirow{3}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multicolumn{2}{c}{**Standardized Coefficients**} & \multirow{3}{*}{**t**} & \multirow{3}{*}{**Sig.**} & \multicolumn{2}{c}{**95.0\% Confidence Interval for B**} \\ \cline{3-3} \cline{5-8}  & & **B** & **Std. Error** & & & & **Lower Bound** & **Upper Bound** \\ \hline \multirow{3}{*}{1} & Constant & \(-0.261\) & \(0.198\) & & \(-1.317\) & \(0.211\) & \(-0.689\) & \(0.167\) \\ \cline{2-8}  & X\({}_{1}\) & \(0.011\) & \(0.005\) & \(0.421\) & \(2.226\) & \(0.044\) & \(0.000\) & \(0.021\) \\ \cline{2-8}  & X\({}_{4}\) & \(0.293\) & \(0.095\) & \(0.581\) & \(3.072\) & \(0.009\) & \(0.087\) & \(0.498\) \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Coefficients with two predictors. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Minimum** & **Maximum** & **Mean** & **Std. Deviation** & **N** \\ \hline Predicted value & \(0.349652\) & \(0.601985\) & \(0.493438\) & \(0.0570784\) & \(16\) \\ \hline Residual & \(-0.1351061\) & \(0.0560455\) & \(0.0000001\) & \(0.0531130\) & \(16\) \\ \hline Std. Predicted value & \(-2.519\) & \(1.902\) & \(0.000\) & \(1.000\) & \(16\) \\ \hline Std. residual & \(-2.368\) & \(0.982\) & \(0.000\) & \(0.931\) & \(16\) \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Statistics-residuals with two predictors. utilizing 2 nd order polynomial as shown (Table 15). As observed, there is robust optimistic correlation among the significant variables and steady-state NaOH transformation to products. The R\({}^{2}\) (observed) showed that the model in Equation (11) is appropriate about 80%, while adjusted R\({}^{2}\) clarifies that the influence of external factors is nearly equal to 18%, which is considerably smaller and equated to the preceding results. The sig. F variation that is equal to 0.001 is considerably <0.05. Also, this value is substantial with an adopted 5% level of significance. The 2 nd order model of polynomial having two independent variables clarifies that F-value is smaller. The significance level is <0.05, which signifies that the impact of each independent variable varies considerably with 5% significance level as represented in Table 16.  
The coefficient in Table 17 is utilized to formulate a relationship applying values of \(\beta\) to obtain the best solution for the hydrolysis of CH\({}_{3}\)COCO\({}_{2}\)H\({}_{5}\) by NaOH. It can be established from Equation (9) that 100% rise of temperature may augment the conversion of NaOH by 2.10% while keeping all other predictors constant. The maximal and minimal NaOH conversion under steady-state condition equal to 63.15% and 28.03% with an average conversion rate of 49.34% was obtained using residual analysis as presented in Table 18. The 63.15% NaOH conversion obtained using Equation (9) is the maximum amongst four models. An increase in reaction NaOH conversion to product capacity was observed using second order polynomial (X\({}_{\text{A}}\) = 0.6315) as compared to a lower value of (X\({}_{\text{A}}\) = 0.60198) using the same predictors with 1 st order polynomials. \[X_{\text{A}}=-2.189+0.021X_{1}+3.480X_{4}+0.001X_{1}^{2}-0.862\,X_{4}^{2}-0.0 29X_{1}X_{4}+\text{error} \tag{12}\]