## 3 Results  
### Descriptive Analyses Results  
Descriptive statistics reveal the frequency and probability of each variable in severe and fatal accidents. Significant relationships between variables and accident severity are identified using [PERSON]'s exact test and the phi coefficient. Also, we generate the presented MCV. We conduct descriptive analyses for each variable within our defined categories (driver, vehicle, roadway, and situation). Detailed analysis results can be found in Appendix A. Figure 3 presents the retrieved phi coefficients between various driver-related variables and the dependent variable, severe casualties. The phi coefficient measures the strength and direction of association between binary variables. Positive values indicate a direct association, where the presence of the variable correlates with an increased likelihood of severe casualties, while negative values indicate an inverse relationship. The figure's colour gradient indicates the strength and direction of the correlation for each variable with severe casualties, where red represents positive values and blue represents negative values. Driving in parallel shows the highest positive phi coefficient (0.604), indicating a strong positive correlation with severe casualties. This suggests that when drivers engage in this behaviour, the likelihood of severe accidents significantly increases. No safety belt applied (0.240) and male drivers (0.133) also exhibit notable positive associations with severe casualties, reinforcing well-established road safety insights that males and lack of seatbelt use are high-risk factors. Age-related factors, such as drivers aged 64 and older (0.082), 45 to 54 years (0.046), and 55 to 64 years (0.044), also show moderate positive correlations with severe casualties, suggesting older age groups are more vulnerable to severe outcomes in accidents. Other factors like hitting a tree (0.062) and fatigue (0.030) also display positive correlations, indicating that these environmental and driver-related conditions contribute to more severe accident outcomes. In contrast, female drivers (\(-\)0.133), drivers aged 19 to 24 years (\(-\)0.085), and those with a probationary driving license (\(-\)0.065) show negative correlations with severe casualties, indicating a lower likelihood of severe injuries for these groups compared to others. Risky behaviours such as speeding (\(-\)0.011) and hitting a stationary vehicle (\(-\)0.005) show slight negative correlations, which may suggest that while these actions are dangerous, they are not as strongly associated with severe casualties in this dataset. Figure 4 presents the phi coefficients between various vehicle-related variables and the dependent variable, severe casualties. Higher engine power correlates positively with severe casualties, as seen with vehicles having 110+ kW engine power (0.053) and 90-110 kW engine power (0.039). This suggests that vehicles with more powerful engines are more likely to be involved in accidents resulting in severe injuries. Similarly, vehicle fire (0.035) shows a positive association. Interestingly, vehicle kilometrage between 150,000 to 200,000 km (0.010) and 0-24 kW engine power (0.006) also present positive associations, suggesting that vehicles with higher mileage and very low engine power might also contribute to accident severity. The variable airbag not deployed shows the most substantial negative correlation (\(-\)0.149), suggesting that in cases where the airbag does not deploy, the likelihood of severe casualties is lower. This does not mean that the absence of airbag deployment directly reduces the risk of injury; rather, it reflects the fact that airbags are typically designed to deploy only in high-impact crashes. In lower-impact accidents, where the airbag does not activate, the injuries tend to be less severe. Therefore, the negative correlation likely indicates that accidents where airbags are not deployed are generally less severe and thus less likely to result in severe casualties.