## 4 Discussion
Particularly impressive is the change in the performance of the CP, which sharply varied from the first to the last predictor for (1) the average permeate flux from 94.014 to 3.549, (2) the COD rejection rate from 27.376 to 3.594, and (3) the cumulative flux decline from 138.756 to 5.000. It is important to note that with this method, factor B is additionally retained for the COD rejection rate response. Furthermore, to model the cumulative flux decline, it is indicated that all four effects should be retained. As can be seen in Tables 15 and 17, the Durbin-Watson statistic estimations for the regression modeling of the average permeate flux and cumulative flux decline suggest that there is no autocorrelation in the residuals. On the contrary, the residual analysis of the regression results for the COD rejection rate (Table 16) suggests that there is a strong negative autocorrelation. In Tables 15-17, it becomes evident that there are no multicollinearity issues among the examined predictors for any of the three quality characteristic responses; the variance inflation factor estimations were very stable, at a value of 1.0, in all instances. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multicolumn{1}{c}{**Enter Factor**} & \multicolumn{1}{c}{**df2**} & \multicolumn{1}{c}{**Sig. F**} & \multicolumn{1}{c}{**Akaike Information**} & \multicolumn{1}{c}{**Amemiya Prediction**} & \multicolumn{1}{c}{**Mallows\({}^{\prime}\) Prediction**} & \multicolumn{1}{c}{**Schwarz Bayesian**} & \multicolumn{1}{c}{**Durbin–Watson**} & \multicolumn{1}{c}{**VIF**} \\ \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**Change**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Statisticistic**} & \multicolumn{1}{c}{**Collinearity**} \\ \hline
1 & 25 & \(<\)0.001 & 81.944 & 0.566 & 27.376 & 84.535 & & 1.0 \({}^{\circ}\) \\
2 & 24 & \(<\)0.001 & 70.077 & 0.365 & 9.142 & 73.964 & & 1.0 \({}^{\circ}\) \\
3 & 23 & 0.011 & 64.294 & 0.295 & 3.594 & 69.478 & 3.307 & 1.0 \({}^{\circ}\) \\ \hline \hline \end{tabular} \({}^{a}\) Predictors: (Constant), D. \({}^{b}\) Predictors: (Constant), D, B. \({}^{c}\) Predictors: (Constant), D, B. \({}^{d}\) Predictors: (Constant), D, A, B. \({}^{d}\) Predictors: (Constant), D, A, B. \({}^{d}\) Predictors: (Constant), D, A, B. \({}^{d}\)  
\end{table} 
Table 16: Change statistics, selection criteria, autocorrelation, and collinearity screening for fitting the COD rejection rate (COD) (IBM SPSS v.29). \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multicolumn{1}{c}{**Enter Factor**} & \multicolumn{1}{c}{**df2**} & \multicolumn{1}{c}{**Sig. F**} & \multicolumn{1}{c}{**Akaike Information**} & \multicolumn{1}{c}{**Amemiya Prediction**} & \multicolumn{1}{c}{**Mallows\({}^{\prime}\) Prediction**} & \multicolumn{1}{c}{**Schwarz Bayesian**} & \multicolumn{1}{c}{**Durbin–Watson**} & \multicolumn{1}{c}{**VIF**} \\ \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**df2**} & \multicolumn{1}{c}{**Shape**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Criterion**} & \multicolumn{1}{c}{**Statistic**} & \multicolumn{1}{c}{**Collinearity**} \\ \hline
1 & 25 & \(<\)0.001 & 148.483 & 0.572 & 94.014 & 151.075 & & 1.0 \({}^{\circ}\) \\
2 & 24 & \(<\)0.001 & 108.319 & 0.129 & 3.549 & 112.207 & 2.067 & 1.0 \({}^{\circ}\) \\ \hline \hline \end{tabular} \({}^{a}\) Predictors: (Constant), A. \({}^{b}\) Predictors: (Constant), A, D. \({}^{c}\) Predictors: (Constant), A, D. \({}^{d}\) Predictors: (Constant), D, A, B. \({}^{d}\) Predictors: (Constant), D, A, B, C.  
\end{table}