## 8 Completion of SSQ (SSQ4) and PQ questionnaires
### Data Preprocessing
This non-parametric test shows that there is no normality for the variables \"PAT\", \"TTC\" and \"Average error VVR\", but there is normality for the variables \"Average error DR\" and \"Average error DVR\" (Table 4). Therefore, only [PERSON]'s correlation will be used to estimate the correlation between variables that result in a normal distribution from the K-S test, while the correlation between variables with normal distribution and those significant in the K-S test will be measured with [PERSON]'s correlation coefficient (Table 5). Since not all numerical explanatory variables fit a normal distribution, assessing the level of correlation with the variable \"Event\" through the point biserial correlation coefficient is discarded. Therefore, binary logistic regression would allow for evaluating the level of association of the numerical predictor variables with the response variable. Thus, requirements such as linearity, normality, or homoscedasticity should not be tested. Considering all continuous explanatory variables, the Wald elimination method is carried backward to leave in the model those variables that significantly affect the dichotomous response variable. The variables with the highest significance on the variable \"Event\" are \"PAT\" (sig = 0.002) and \"Average error DVR\" (sig = 0.03). Likewise, the results in (Table 5) justify the independence between these two predictors, so that \"PAT\" and \"Average error DVR\" will be included in the classification models to be addressed in the following sections. In the case of categorical explanatory variables, the Chi-Square test allows us to evaluate whether the variables are independent of each other and of the response variable. In this case, the null hypothesis (H0) is that the variables are independent, while the alternative hypothesis (H1) is that there is a relationship between this pair of variables. It is not necessary in any case to use [PERSON]'s exact test since the expected counts less than five are less than 20% in all cases (Table 6). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PAT**} & \multirow{2}{*}{**TTC**} & \multirow{2}{*}{**Average Error**} & \multirow{2}{*}{**Average Error DVR**} & \multirow{2}{*}{**Average Error VVR**} \\  & & & **DR** & **DVR** \\ \hline \textless{}0.001 & 0.0388 & 0.2 & 0.062 & 0.031 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Significance level of the Kolmogorov–Smirnov test for each numerical explaining variable. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **PAT** & **TTC** & **Average Error DR** & **Average Error DVR** & **Average Error VVR** \\ \hline
**PAT** & 1 & 0.128 (sig = 0.343) & \(-\)0.106 (sig = 0.433) & \(-\)0.117 (sig = 0.388) & \(-\)0.141  
In Figure 9, the cases with a greater miscalculation in distances during the experimental session in VR tend not to react to potential run-over situations (Q1 higher than the interquartile range of the other two types of reaction). This is evidence that, when perceiving the vehicle closer than it really is, the pedestrian tends to remain blocked or paralyzed, thus nullifying their response capacity. In the scatter plot in Figure 10, notice how there is a great accumulation of avoidance cases for higher percentages of time with greater attention, while those who spend less time looking at an angle less than that defined as the limit tend to be hit. Some cases contradict this hypothesis, so they cannot be explained without the remaining estimators.