# 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
\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 & 0.541 & 0.050 & 118.150 & 1 & \(<\)0.001 & 1.718 & 1.559 & 1.895 \\ Black list & 0.932 & 0.045 & 432.550 & 1 & \(<\)0.001 & 2.538 & 2.325 & 2.771 \\ Out of list & 0.865 & 0.101 & 73.428 & 1 & \(<\)0.001 & 2.375 & 1.949 & 2.895 \\ Tank & 0.595 & 0.129 & 21.199 & 1 & \(<\)0.001 & 1.813 & 1.407 & 2.336 \\ Cargo & 1.106 & 0.110 & 101.551 & 1 & \(<\)0.001 & 3.022 & 2.437 & 3.748 \\ Special Purposes & 0.614 & 0.129 & 22.532 & 1 & \(<\)0.001 & 1.848 & 1.434 & 2.382 \\ Age & 0.040 & 0.002 & 544.952 & 1 & \(<\)0.001 & 1.041 & 1.038 & 1.045 \\ Atlantic ports & 0.737 & 0.036 & 419.650 & 1 & \(<\)0.001 & 2.090 & 1.948 & 2.243 \\ Constant & \(-\)4.108 & 0.121 & 1156.048 & 1 & \(<\)0.001 & 0.016 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Parameters estimation regarding the indicator HRSC2. * 1.211 times higher if the ship is a cargo ship than it is a passenger ship (with a significance of \(p\) = 0.026). * 0.596 times lower if the ship is a tanker than it is a passenger ship. * 0.660 times lower if the ship is a special-purpose ship than it is a passenger ship. * 1.339 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 a ship will face a radio inspection in which deficiencies are linked to poor competencies of OOWs regarding GMDSS skills:  
* 1.024 times greater for each year of increase in the ship's age. * 1.026 times greater for each year that passes on the calendar compared to 2015. The Distribution of Independent Variables Ship-Flag, Ship-Type, Port Situation, and Ship-Age in the Subsets HRSC2 and HRSC3  
The chi-square test is employed to check if there is any relationship between two categorical variables. The analysis consists of comparing the goodness of feet between the expected distribution of flags, ship types, port location, and ship ages and the observed distribution of the two subsets (Tables 11 and 12 show the distributions). The age of the ships has been categorized into 5 groups in 10-year steps (Table 12). That is, what is expected from the total database and what is observed in the subset are compared. The null hypothesis is that the proportion is the same, so there are no statistically significant differences in the distribution of the variables in the subsets with respect to the distribution of variables in the entire dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**FLAG**} & \multicolumn{2}{c}{**Total Database**} & \multicolumn{2}{c}{**HRSC2 Subset**} & \multicolumn{2}{c}{**HRSC3 Subset**} \\ \cline{2-7}  & **N of Ships** & **\%** & **N of Ships** & **\%** & **N of Ships** & **\%** \\ \hline White & 9224 & 70.0\% & 658 & 39.5\% & 810 & 47.0\% \\ Grey & 1690 & 12.8\% & 281 & 16.9\% & 261 & 15.1\% \\ Black & 1981 & 15.0\% & 659 & 39.6\% & 617 & 35.8\% \\ Out of the list & 283 & 2.1\% & 67 & 4.0\% & 37 & 2.1\% \\ \hline \multirow{2}{*}{**SHIP TYPE**} & \multicolumn{2}{c}{**Total Database**} & \multicolumn{2}{c}{**HRSC2 Subset**} & \multicolumn{2}{c}{**HRSC3 Subset**} \\ \cline{2-7}  & **N of Ships** & **\%** & **N of Ships** & **\%** & **N of Ships** & **\%** \\ \hline Passenger & 615 & 4.7\% & 47 & 2.8\% & 69 & 4.0\% \\ Tanker & 1707 & 13.0\% & 125 & 7.5\% & 85 & 4.9\% \\ Cargo & 9667 & 73.4\% & 1363 & 81.9\% & 1489 & 86.3\% \\ Special purposes & 1189 & 9.0\% & 130 & 7.8\% & 82 & 4.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Crosstabulation of country flags, and ship types using the database of ships.