# Analyzing Port State Control Data to Explore Future Improvements to GMDSS Training
## 3 Results
### Binary Regression Analysis Results Were Applied to Databases of Table 4
For the categorical variables, the first \(p\)-value indicates whether there is an association between that independent variable and the dependent and the other rows compare the individual categories with the reference category. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Omnibus Tests of** & & & \\
**Model Coefficients** & & & \\ \hline Chi-square Model & 1171.507 & 3284.189 & 1295.02 \\ df & 8 & 8 & 9 \\ \(p\)-value & \textless{}0.001 & \textless{}0.001 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of the significance of the test. Figure 2: The time-changing relationship between the number of cases in both subsets (HRSC2 in red and HRSC3 in blue) per year as a ratio using the total number of inspections per year (data from Tables 2 and 3). Table 9 shows that all variables are significant with \(p<0.001\). References for every category are the white list (for flag), passenger ships (for ship type), and Atlantic ports (for port location). For categorical variables, it can be affirmed that the model predicts that the odds that a ship facing a radio inspection resulting in detention are as follows:  
* 2.54 times higher if the ship is black-listed than white-listed. * 2.38 times higher if the ship is grey-listed than white-listed. * 1.72 times higher if the ship is \"out-of-list\" than white-listed. * 3.02 times higher if the ship is a cargo ship than it is a passenger ship. * 1.85 times higher if the ship is a special-purpose ship than it is a passenger ship. * 1.81 times higher if the ship is a tanker than it is a passenger ship. * 2.09 times higher if the ship is inspected at a Mediterranean port than at Atlantic ports. For continuous variables, it can be affirmed that the model predicts that the odds that a ship facing a radio inspection will result in detention are as follows:  
* 1.041 times greater for each year of increase in the ship's age. Table 10 shows that all variables are significant with \(p<0.001\), except the category of \"Out of the list\" of the explanatory variable \"Ship Flag\". References for every category are the white list (for Flag), passenger ships (for Ship Type), and Atlantic ports (for Port Location). For categorical variables, it can be affirmed that the model predicts the odds that a ship will face a radio inspection in which deficiencies are linked to poor competencies of OOWs regarding GMDSS skills:  
* 1.895 times higher if the ship is black-listed than white-listed. * 1.302 times higher if the ship is grey-listed than white-listed. * 1.243 higher if the ship is not included in any list than white-listed (with a lack of significance of \(p=0.062\)) \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **B** & **S.E.** & **Wald** & **df** & **Sig.** & **Exp(B)** & **Lower** & **Upper** \\ \hline Grey list & 0.264 & 0.052 & 25.629 & 1 & \(<\)0.001 & 1.302 & 1.175 & 1.442 \\ Black list & 0.639 & 0.047 & 188.035 & 1 & \(<\)0.001 & 1.895 & 1.729 & 2.076 \\ Out of list & 0.217 & 0.116 & 3.479 & 1 & 0.062 & 1.243 & 0.989 & 1.561 \\ Tank & \(-\)0.517 & 0.113 & 20.944 & 1 & \(<\)0.001 & 0.596 & 0.478 & 0.744 \\ Cargo & 0.192 & 0.086 & 4.948 & 1 & 0.026 & 1.211 & 1.023 & 1.434 \\ Special Purposes & \(-\)0.416 & 0.114 & 13.329 & 1 & \(<\)0.001 & 0.660 & 0.528 & 0.825 \\ Age & 0.024 & 0.002 & 199.266 & 1 & \(<\)0.001 & 1.024 & 1.021 & 1.028 \\ Atlantic ports & 0.292 & 0.036 & 65.664 & 1 & \(<\)0.001 & 1.339 & 1.248 & 1.437 \\ Year & 0.026 & 0.003 & 54.963 & 1 & \(<\)0.001 & 1.026 & 1.019 & 1.033 \\ Constant & \(-\)4.108 & 0.121 & 1156.048 & 1 & \(<\)0.001 & 0.016 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Parameters estimation regarding the indicator HRSC3.