## 4 Discussion
\begin{table}
\begin{tabular}{l l|c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Kolmogorov–Smirnov Test 1**  & \multicolumn{3}{c}{**Shapiro–Wilk Test**} \\  & \multicolumn{3}{c}{**Statistic**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Sig.**} & \multicolumn{1}{c}{**Statistic**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Sig.**} \\ \hline \multirow{3}{*}{J\({}_{\text{P}}\)} & \(-\)**1** & 0.219 & 9 & 0.200 1  & 0.820 & 9 & 0.035 \\  & \(0\) & 0.220 & 9 & 0.200 1  & 0.874 & 9 & 0.137 \\  & \(1\) & 0.190 & 9 & 0.200 1  & 0.913 & 9 & 0.334 \\  & \(-\)**1** & 0.124 & 9 & 0.200 1  & 0.970 & 9 & 0.897 \\  & \(0\) & 0.180 & 9 & 0.200 1  & 0.930 & 9 & 0.482 \\  & \(1\) & 0.327 & 9 & 0.006 & 0.809 & 9 & 0.026 \\  & \(-\)**1** & 0.193 & 9 & 0.200 1  & 0.901 & 9 & 0.256 \\  & \(0\) & 0.312 & 9 & 0.012  & 0.774 & 9 & 0.010 \\  & \(1\) & 0.185 & 9 & 0.200 1  & 0.903 & 9 & 0.269 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Tests of normality (Kolmogorov–Smirnov test and Shapiro–Wilk test at \(\alpha=0.05\)) for data partitioned according to the three settings of factor D (MWCO) for (A) average permeate flux (J\({}_{\text{P}}\)), (B) COD rejection rate (COD), and (C) cumulative flux decline (SFD) (IBM SPSS v.29). \begin{table}
\begin{tabular}{l l|c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Kolmogorov–Smirnov Test 1**  & \multicolumn{3}{c}{**Shapiro–Wilk Test**} \\  & \multicolumn{3}{c}{**Statistic**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Sig.**} & \multicolumn{1}{c}{**Statistic**} & \multicolumn{1}{c}{**df**} & \multicolumn{1}{c}{**Sig.**} \\ \hline \multirow{3}{*}{J\({}_{\text{P}}\)} & \(-\)**1** & 0.219 & 9 & 0.200 1  & 0.820 & 9 & 0.035 \\  & \(0\) & 0.220 & 9 & 0.200 1  & 0.874 & 9 & 0.137 \\  & \(1\) & 0.190 & 9 & 0.200 1  & 0.913 & 9 & 0.334 \\  & \(-\)**1** & 0.124 & 9 & 0.200 1  & 0.970 & 9 & 0.897 \\  & \(0\) & 0.180 & 9 & 0.200 1  & 0.930 & 9 & 0.482 \\  & \(1\) & 0.327 & 9 & 0.006 & 0.809 & 9 & 0.026 \\  & \(-\)**1** & 0.193 & 9 & 0.200 1  & 0.901 & 9 & 0.256 \\  & \(0\) & 0.312 & 9 & 0.012 & 0.774 & 9 & 0.010 \\  & \(1\) & 0.185 & 9 & 0.200 1  & 0.903 & 9 & 0.269 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Tests of normality (Kolmogorov–Smirnov test and Shapiro–Wilk test at \(\alpha=0.05\)) for data partitioned according to the three settings of factor C (temperature) for (A) average permeate flux (J\({}_{\text{P}}\)), (B) COD rejection rate (COD), and (C) cumulative flux decline (SFD) (IBM SPSS v.29). Figure 8: Violin plot factorial screening for the average permeate flux (\(\rm{J_{P}}\)) at all three preset levels. Figure 10: Violin plot factorial screening for the cumulative flux decline (SFD) at all three preset levels. Figure 9: Violin plot factorial screening for the COD rejection rate (COD) at all three preset levels. Screening results that take into account the sequential evaluation of change statistics, aa well as the accompanying significance of the F-test score change when successively entering and retaining additional regressors, are tabulated for all three quality characteristics in Tables 15-17. The inclusion of particular factorial contributions in the active effects list is justified, and it is individually accounted for. In all cases, the achieved significance that measured the change in the modeling effort was below an error rate of \(\alpha=0.05\) (IBM SPSS v.29). All regression models improved significantly with the addition of key factorial contributions, as indicated by the Akaike Information Criterion (AIC), the Amemiya Prediction Criterion (PC), the Mallows' Prediction Criterion (CP), and the Schwartz Bayesian Criterion (SBC) (IBM SPSS v.29). There is a collective agreement among the four model selection criteria about finalizing the group of statistically important effects, which were deemed worth retaining.