## 4 Descriptive Analysis of Data
### ANOVA and Non-Parametric Tests  
Analysis of Variance (ANOVA) is a statistical method used to determine and test if there are significant differences between the means of two or more groups, based on one dependent variable and one or more independent variables. In this research, \"use of AI\" serves as the dependent variable, while the various areas/phases of aviation construction projects are considered the independent variables. For testing purpose, a significance level of 0.05 is adopted. If the \(p\)-value is less than 0.05, the result is deemed \"significant\" suggesting that there is a statistically significant difference between the group means. Conversely, if the \(p\)-value exceeds 0.05, the result is regarded as \"not significant\". Since we are trying to test the impact of AI on different aspects of construction aviation projects via the measure of the experience and use of AI in this sector, a multivariate analysis is required for the different inferential statistics needed. One multivariate analysis that can be considered here is the Discriminant Analysis (DA) that is used to test if the gathered data can discriminate between those who have used AI and those who haven't. By this test, we can verify that the data can discriminate between the two groups and hence, the impact of knowledge/Experience/Use of AI can be noticed if the variables are capable of discriminating between the two groups. The results of the DA are shown in Table 6. The Canonical correlation (0.778) seems to be high, indicating the capability of the selected variable in discriminating the use of AI; however, this needs to be supported by the significance through [PERSON]'s test as shown in Table 7. Since the \(p\)-value is way less than 0.05, the null hypothesis of zero correlation is rejected, indicating that the selected variables are correlated to the variable \"experience and use of AI\". Table 8 shows the pooled-within-groups correlations between discriminating variables and standardized canonical discriminant functions. Note that the variables are ordered by the absolute size of correlation within the discriminant function. The AI-driven maintenance scheduling seems to have the most effect on discrimination, while AI in quality \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Eigenvalues**} \\ \hline
**Function** & **Eigenvalue** & **\% of Variance** & **Cumulative \%** & **Canonical Correlation** \\ \hline
1 & 1.538 [FOOTNOTE:]Footnote : First 1 canonical discriminant functions were used in the analysis.[ENDFOOTNOTE] & 100.0 & 100.0 & 0.778 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the Canonical Discriminant Function. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Wilks’ Lambda**} \\ \hline
**Test of Function(s)** & **Wilks’ Lambda** & **Chi-Square** & **df** & **Sig.** \\ \hline
1 & 0.394 & 74.053 & 39 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Wilk’s Lambda Test. control processes particularly in aviation construction projects seems to be the least influential in discrimination. To test whether the factor \"experience in AI\" has an impact on the safety protocols in aviation construction projects, an ANOVA test can be conducted for this purpose; however,