## 3 Results and Discussion
### Analysis of Features
#### 3.3.2 ANOVA Analysis  
The One-way ANOVA analysis is performed on the MDS variable (Table 8). The analysis suggests significant results (\(p\) = 0.004, VS-MPR = 18.418). The analysis outcomes show a medium effect size (\(\eta^{2}\) = 0.136, \(\omega^{2}\) = 0.103), suggesting that the MDS predictor explains 13.6% of the total variance. The visualisation of categories added further insights into the predictor (Figure 7a). Given the presence of an outlier in the MDS1 category, the evidence suggests conflicting findings and presumably statistically significant results due to the confounding effects of the company size. The ANOVA analysis of MDS categories on the performance of MTBF and MTTR did not report statistically significant results. Furthermore, the evidence also suggests inequality of variances (\(F\) = 4.527, \(p\) = 0.002), while the QQ plot suggests deviation of residuals (Figure 7b). Thus, we used the Kruskal-Wallis \(H\) test as a non-parametric alternative to the ANOVA. The results report significant findings (\(H\) = 23.730, \(p\) < 0.001). In addition, we perform post hoc pairwise comparisons using [PERSON]'s test (with 1000 bootstrapping) to uncover the difference between categories (Table 9). The results suggest there is a statistically significant difference between MDS1-MDS3 (\(p\) < 0.001, RBC = \(-\)0.429) and MDS1-MDS4 (\(p\) < 0.001, RBC = \(-\)0.733). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**H-C \({}^{*}\)** & **Cases** & **SS \({}^{*}\)** & **df** & **MS \({}^{*}\)** & \(F\) & \(p\)** & **VSMPR \({}^{*}\)** & \(\eta^{2}\)** & \(\eta^{2}\)**\(p\)** & \(\omega^{2}\) \\ \hline None & MDS\_Coded & \(1.653\times 10^{7}\) & 4 & \(4.133\times 10^{6}\) & 4.172 & 0.004 & 18.418 & 0.136 & 0.136 & 0.103 \\  & Residuals & \(1.050\times 10^{8}\) & 106 & 990,696.461 & & & & & & \\ BF \({}^{*}\) & MDS\_Coded & \(1.653\times 10^{7}\) & 4 & \(4.133\times 10^{6}\) & 4.665 & 0.002 & 26.19 & 0.136 & 0.136 & 0.103 \\  & Residuals & \(1.050\times 10^{8}\) & 63.083 & \(1.665\times 10^{6}\) & & & & & & \\ Welch & MDS\_Coded & \(1.653\times 10^{7}\) & 4 & \(4.133\times 10^{6}\) & 3.945 & 0.017 & 5.397 & 0.136 & 0.136 & 0.103 \\  & Residuals & \(1.050\times 10^{8}\) & 19.388 & \(5.416\times 10^{6}\) & & & & & & \\ \hline \hline \end{tabular} \({}^{*}\)H-C = Homogeneity Correction; SS = Sum of Squares; MS = Mean Square; BF = Brown–Forsythe; VSMPR = Vork–Sellke Maximum p-Ratio.  
\end{table} 
Table 8: ANOVA analysis of MDS and WOM. Figure 7: Pairwise comparison of (**a**) MDS categories (1 = Operators, 2 = Technicians, 3 = Engineers, 4 = Managers, and 5 = hydraulic system Specialists) and WOM metric and (**b**) QQ plot of residuals. Considering the MAP effects on performance metrics, the findings are as follows. The influence of MAP on WOM and MTRF performance did not provide significant results. As for the MTBF (Table 10), there is an effect between MAP and MTBF (\(p\) = 0.027, \(\eta^{2}\) = 0.097), even after homogeneity correction. However, the MAP accounts for about 9.7% of the total variance in MAP. After plotting the results (Figure 8a), the distributions suggest a higher theoretical probability of increasing MTBF with advanced MAP. The normality assumption is not violated (Figure 8b), but [PERSON]'s test (\(p\) = 0.022) suggests heterogeneous variance, which, after correction, the evidence suggests statistically significant results. The results suggest the same outcome after performing a post hoc pairwise comparison using parametric [PERSON] and non-parametric [PERSON]'s test (Table 11) using 1000 bootstrapping. Namely, in both cases, there is a statistically significant difference between MAP1 and