## 5 Pre-Test and Pre-Test Data Analysis
### Pre-Test Data Analysis  
The real-time VRET system data, shown in Figures 7 and 8, and the pre-test subject's bio-data, are shown in Figures 9 and 10. For the current prototype of VRET for driving phobia, the bio-data of each subject are monitored and analyzed for trends and correlations between bio-data and system data. The data spike abnormalities are monitored during the pre-testing experiment sessions. The goal is to improve the effectiveness of the therapy design of the VRET immersive system based on data analytic outcomes to avoid levels of driving conditions showing insignificant bio-effects or causing overly dramatic effects (i.e., lacking the feature of gradual systematic desensitization). The following discussion describes the statistical analysis results of the system's pre-test experiments with 31 subjects. The Pearson correlation coefficient has been applied to measure the strength of linear association between the VRET system data and the subject's bio-data. The result indicates that the correlations are relatively small with most coefficients falling between \(-0.3\) and \(0.3\). Nonparametric test for gender differences compared the 14 males and 17 female subjects that participated in the pre-test experiment. The Mann-Whitney U test was used to test if there was a statistically significant difference between the overall satisfaction medians of the male and female groups. The null hypothesis is \(\mathrm{n}_{male}=\mathrm{n}_{female}\), using a significance level of \(\alpha=0.05\), the \(p\)-values related to four kinds of bio-data are all higher than alpha value (0.05), so there is insufficient evidence to conclude a statistically significant difference between the overall satisfaction medians of the male and female groups. The Mann-Whitney U test was also used to test if there is a statistically significant difference between the overall satisfaction medians of the high anxiety and low anxiety groups. Subjects were divided into two groups, high anxiety groups and low anxiety groups, according to the rating of \"Self-rating fear of driving scale\" filled out before the pre-test experiment. Eleven subjects were in the high anxiety group and 20 subjects were in the low anxiety group. The null hypothesis is \(\mathrm{n}_{high}=\mathrm{n}_{low}\), using a significance level of \(\alpha=0.05\), three out of the four \(p\)-values related to bio-data (heart rate, respiration, and skin conductance) are lower than alpha value (0.05), so there is statistical evidence that there is a difference between the overall satisfaction medians of the two anxiety types. The VRET system does affect the subject's bio-data according to their degree of fear of driving. One-way ANOVA tests comparing means of bio-data in seven scenarios are used to estimate the 95% confidence interval and determine whether the associated population means in different scenarios are significantly different. The results are presented in Figure 11a,c,e,g. [PERSON]'s least significant difference method for multiple comparisons was applied to examine which means at different levels are different, as presented in Figure 11b,d,f,h. The bio-feedback data of the one-way ANOVA interval plots, presented in Figure 11, indicate that pre-test subjects become increasingly anxious as the fear level of the scenarios increases (shown by heart rate, temperature, and skin conductivity trends). For detailed information from the ANOVA results, multiple comparisons analysis between different levels was also conducted, as higher fear levels tend to be assigned to the same groups and lower fear levels tend to be assigned to the same groups, a significant difference in the sample means between higher levels and lower levels was demonstrated. This trend supports the hypothesis that increases in the severity of pre-existing phobias increase the subject's bio-feedback responses.