## 4 Results
### Results of Regression Analysis  
Regression analysis was also conducted to determine the extent to which civil protection is predicted by the different aspects of cyber security technologies, cybercrime reduction, and government role in cyber security, and the results are presented in Table 9. Regression analysis was used to analyze the amount to which the independent variables (cyber security technology, cybercrime reduction, and government engagement in cyber security) contribute to strengthening civil safety in Europe based on different projected values. The positive multiple correlation coefficient (R) of 0.702 was an indication that the three independent variables in this study were greatly associated with civil protection. Furthermore, the R-Square value revealed that the three independent factors resulted in a 66.1% shift in civil protection. The one-way ANOVA in Table 10 was used to assess if the three independent variables were superior predictors of the dependent variable or whether the linear regression model well fitted the data. F (3,342) = 352.413, \(p\) \(<\) 0.05 demonstrates that the model and data are sufficiently matched. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Sum of Squares** & **df** & **Mean Square** & **F** & **Sig.** \\ \hline Regression & 36.040 & 3 & 18.361 & 352.413 & 0.016 \\ \hline Residual & 30.108 & 342 & 0.027 & \\ \hline Total & 66.148 & 345 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: ANOVA analysis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \
\begin{tabular}{c} **Cyber Security** \\ **Technologies** \\ \end{tabular} & **Cybercrime Reduction** &
\begin{tabular}{c} **Government Role** \\ **in Cyber Security** \\ \end{tabular} & **Civil Protection** \\ \hline Cyber security technologies & 1 & & & \\ \hline \multirow{2}{*}{Cybercrime reduction} & 0.548 ** & 1 & & \\ \cline{2-5}  & 0.00 & & & \\ \hline \multirow{2}{*}{Government role in cyber security} & 0.551 * & 0.649 * & 1 & \\ \cline{2-5}  & 0.02 & 0.000 & & \\ \hline \multirow{2}{*}{Civil protection (dep.)} & 0.482 * & 0.631 * & 0.546 * & 1 \\ \cline{2-5}  & 0.00 & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}  
* respectively indicate statistical significance at a 5% significance level.  
\end{table} 
Table 8: Cross-tabulation of aspects of cyber security technologies, cybercrime reduction, and government role in cyber security and civil protection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **R** & **R Square** & **Adjusted R Square** & **Std. Error of the Estimate** \\ \hline
0.702 a & 0.661 & 0.659 & 0.37412 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Model Summary. In Table 11, the model's unstandardized coefficients were investigated to determine the impact of cyber security technology, cybercrime reduction, and the involvement of the government in cyber security on civil protection. The beta coefficient of cyber security technologies was 0.281, implying that a unit change in cyber security technologies resulted in a 28.1% increase in civil protection. Similarly, the beta coefficient of cybercrime was 0.186, meaning that a unit change in cybercrime reduction resulted in an 18.6% increase in civil protection. Finally, a unit shift in the role of the government in cyber security would result in a 19.2% increase in civil protection. The beta coefficient (\(\beta\)1) was 0.315, and the \(p\)-value (0.001) was less than the significance level (0.05); hence, we accepted hypothesis 1 that cyber security technologies positively affects civil protection. The beta coefficient (\(\beta\)2) was 0.117 and significant at 0.05 (\(p=0.014\)); hence, we accepted hypothesis 2 that cybercrime reduction practices have a positive influence on civil protection. The beta coefficient (\(\beta\)3) was 0,341, and the variable was significant where \(p\)-value = 0.000, indicating a substantial relationship between government role in cyber security and civil protection.