## 3 Results and Discussion
### Analysis of Features
#### 3.3.1 Independent Samples Statistic  
Firstly, an analysis of the FAP predictor is performed. The split between indoor and outdoor (outsource) Failure Analysis Personnel is performed. Both parametric and non-parametric results are reported due to possible violation of normality assumptions reported via the Shapiro-Wilk test. For instance, WOM_Est (\(W\) = 0.763, \(p\) < 0.001) and MTTR_Est (\(W\) = 0.878, \(p\) < 0.001) report such cases. In addition, the student \(t\)-test statistic is discussed if there was no violation of normality and equality of variances; elsewhere, the [PERSON]'s U test and [PERSON]'s test are reported, respectively. The results suggest statistically significant results of WOM and MTBF (Table 4). However, no statistically significant results were reported considering the impact of outsourced personnel on reducing availability, i.e., MTTR (\(p\) = 0.245). Visualised results (Figure 5) suggest a deviation comparing the WOM_Est of indoor and outsourced maintenance for failure diagnosis. Even so, considering the non-parametric results reported via Mann-Whitney (\(U\) = 1985, \(p\) = 0.009, RBS = 0.272), a decrease measured by wasted oil monthly (Figure 5a) exists. Next, measuring MTBF (Figure 5b), there is evidence of statistical significance (\(t\) = \(-\)3.585, \(p\) < 0.001, \(d\) = \(-\)0.681) when comparing indoor and outsourced FAP. This suggests that outsourcing maintenance may positively impact sustainability and improve operational efficiency but will not guarantee an improved time-to-repair metric. Considering the analysis of DAT (Table 5), the MTBF reports statistically significant results (\(t\) = \(-\)2.519, \(p\) = 0.025, \(U\) = 315, \(p\) = 0.020) comparing the cases with and without data \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Test** & **Value** & \(p\) & **VS-MPR** & **SE Diff.** & **Effect** & **95\%CI\({}_{\text{L}}\)** & **95\%CI\({}_{\text{U}}\)** \\ \hline WOM & Student & 3.769 & 0.001 & 167.711 & 188.558 & 0.715 & 0.330 & 1.098 \\  & Welch & 3.798 & 0.001 & 141.667 & 187.125 & 0.718 & 0.324 & 1.108 \\  & M-W & 1958.5 & 0.009 & 8.653 & & 0.272 & 0.063 & 0.458 \\ MTTR & Student & 1.170 & 0.245 & 1.068 & 0.472 & 0.222 & \(-\)0.152 & 0.595 \\  & Welch & 1.174 & 0.243 & 1.070 & 0.471 & 0.222 & \(-\)0.151 & 0.595 \\  & M-W & 1654.5 & 0.496 & 1.000 & & 0.074 & \(-\)0.140 & 0.282 \\ MTBF & Student & \(-\)3.585 & 0.001 & 95.728 & 134.985 & \(-\)0.681 & \(-\)1.062 & \(-\)0.296 \\  & Welch & \(-\)3.586 & 0.001 & 96.211 & 134.915 & \(-\)0.681 & \(-\)1.062 & \(-\)0.296 \\  & M-W & 919 & 0.001 & 179.355 & & \(-\)0.403 & \(-\)0.567 & \(-\)0.209 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Independent sample \(t\)-test statistic of FAP. Figure 5: Independent sample statistic of indoor (green) and outdoor (orange) FAP considering (a) WOM_Est distributions showing violation of normality (\(W\) = 0.763, \(p\) < 0.001) and equality of variance violation using Brown–Forsythe test (\(F\) = 15.442, \(p\) < 0.001); and (b) MTBF_Est distributions suggesting skewness of normality of indoor FAP (\(W\) = 0.981, \(p\) = 0.002) and homogeneity of variances (\(F\) = 0.080, \(p\) = 0.778). Note: FAP_Binary (1) = Outdoor; FAP_Binary (0) = Indoor failure analysis. analysis tools used in fault detection and diagnosis. When performing the analysis, the lack of a strong effect is presumably due to the presence of an outlier (Figure 6a). Even so, the results suggest slight skewness, although violation in variance homogeneity is not reported. Thus, with caution, we can assume that the results would increase with a larger sample size. The reported effect (RBC = \(-0.427\) with 95%CI [\(-0.674\), \(-0.062\)]) and diagnosticity of reported \(p\)-value = 0.031 using VS-MPR [44; 45] suggest a maximum of 3.386 odds in favour of rejecting the null, i.e., in favour of H\({}_{1}\) over H\({}_{0}\).