## 4 Result
### Predictive Modelling of HRV and Stress
#### 4.3.2 Ordinal Logistic Regression for the PSS  
To investigate the influence of HRV features across all segments, ages, and experience levels of the MW related to physical and performance demands on the navigators' perceived stress, an ordinal logistic regression model was established. Combined with the results of the correlation analysis, the age group and experience group, although presenting weak negative correlations, remain essential demographic factors that may influence perceived stress. The MRR, LF/HF, and NASA-TLX results for the MW (MD, MD, and OP) were selected as explanatory variables. The dependent variable was each of the navigator's perceived stress level, labelled 1 to 3, indicating perceived stress levels ranging from low to high. Independent variables were entered into the model individually to assess their individual contributions. MRR2 and LF/HF2 exhibited the strongest positive correlations with perceived stress (\(\rho\) = 0.006, \(p\) \(<\) 0.05 and \(\rho\) = 0.035, \(p\) \(<\) 0.05), indicating a significant relationship between higher HRV feature values in segment 2 and increased stress levels. The results are shown in Table 16. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Coefficient (\(\beta\))** & **Std. Error** & **t-Value** & **\(p\)-Value** \\ \hline Intercept & 340.362 & 133.769 & 2.544 & 0.0193 * \\ Age & 7.426 & 3.076 & 2.414 & 0.0255 * \\ Experience & 1.956 & 3.107 & 0.63 & 0.5361 \\ PSS & \(-\)2.464 & 2.781 & \(-\)0.886 & 0.3861 \\ \hline \hline \end{tabular}  
* R\({}^{2}\) = 0.334, adjusted R\({}^{2}\) = 0.234, F (3, 20) = 3.34, \(p\) = 0.077, Durbin-Watson = 2.373,
* \(p\) \(<\) 0.05.  
\end{table} 
Table 15: Multiple linear regression results predicting the MRR in segment 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Coefficient (\(\beta\))** & **Std. Error** & **t-Value** & **\(p\)-Value** \\ \hline Intercept & \(-\)1.466 & 4.630 & \(-\)0.317 & 0.754849 \\ Age & 0.037 & 0.106 & 0.351 & 0.729462 \\ Experience & \(-\)0.087 & 0.108 & \(-\)0.808 & 0.428483 \\ PSS & 0.375 & 0.096 & 3.894 & 0.0009 ** \\ \hline \hline \end{tabular}  
* R\({}^{2}\) = 0.441, adjusted R\({}^{2}\) = 0.357, F (3, 20) = 5.26, \(p\) = 0.007, Durbin-Watson = 2.614,
* \(p\) \(<\) 0.01.  
\end{table} 
Table 14: Multiple linear regression results predicting the LF/HF ratio for segment 2. The regression coefficient of the 'low perceived stress state' indicates the model intercept of the low perceived stress state relative to the moderate perceived stress state and high perceived stress state. The regression coefficient of the'moderate perceived stress state' indicates the model intercept of the low perceived stress state and moderate perceived stress state relative to the high perceived stress state. With other conditions unchanged, the odds ratios (OR) is a measurement of change of a variable due to the increase in another variable by one unit. The OR represents the change in the dependent variable resulting from a one-unit increase in an independent variable, while holding all other factors constant. In ordinal logistic regression, it is essential to conduct a parallel line test to assess whether the coefficients of the independent variables are equal. This hypothesis is evaluated using Chi-square statistics and their associated \(p\)-values, which assist in confirming the model's validity [53, 54]. To verify the ordinal logistic regression model's suitability, the proportional odds (parallel lines) assumption was tested. The likelihood ratio test was performed to assess whether the partial regression coefficients of all independent variables in the model were equal to zero. The result of the parallelism test indicated that the regression equations were parallel to each other (\(\chi^{2}=0\), \(p=1>0.05\)), meaning that the parallelism assumption held. The overall model fit was evaluated by comparing the two log-likelihoods, with the result showing \(\chi^{2}=25.284\) and \(p=0.0309<0.05\), indicating that the model fits well.