## 4 Results  
Table 2 shows independent variables such as status, bike-sharing service, and average driving distance. These variables are categorical (nominal or ordinal) variables. The sample is random. All responses are anonymous. The main part of the questionnaire is aimed at users of alternative means of transport. The total sample consists of 304 respondents, but only 162 respondents use a shared bike for relaxing, commuting to school, or working. Although this sample size may seem small to draw general conclusions, several factors make it relevant to this study. Firstly, the selection of respondents focused on active users of shared bikes ensured that the data obtained were directly relevant to the travel purposes analyzed. Although the sample size is limited, the research is focused on a specific group of users, increasing the accuracy of the information obtained. Secondly, multinomial logistic regression, which is suitable for predicting categories based on multiple independent variables, was used to analyze the data. The results show that the statistical models have sufficient explanatory power (e.g., Nagelkerke R\({}^{2}\) = 0.513), indicating that the model was able to explain more than half of the variability in the dependent variable. Statistical tests such as Chi-square, Pearson's test, and deviance test confirm that the model is statistically significant, indicating that the results are reliable even with a smaller sample size. However, the authors of the study admit that the limited sample size represents a certain disadvantage, and in the future, they will try to reach a larger group of respondents in order to make the conclusions more robust. As can be seen, most respondents are students (more than 50%). This questionnaire (see Appendix A) was intended for people from 15 to 62 years old. However, the status divides respondents into employees and students, and both groups have the same representation. The results show that the respondents most often use a shared bicycle to commute to work (38% of all). On the other hand, the fewest respondents ride a shared bike for leisure. In \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nominal Variables** & **Category** & **N** & **\%** \\ \hline \multicolumn{4}{c}{Dependent variable} \\ \hline \multirow{4}{*}{Purpose of riding} & Relax & 46 & 28.40\% \\ \cline{2-4}  & School & 55 & 34.00\% \\ \cline{2-4}  & Job & 61 & 37.70\% \\ \hline \multicolumn{4}{c}{Independent variables} \\ \hline \multirow{4}{*}{Status} & Employee & 79 & 48.80\% \\ \cline{2-4}  & Student & 83 & 51.20\% \\ \cline{2-4}  & A & 54 & 33.30\% \\ \cline{2-4}  & B & 17 & 10.50\% \\ \cline{2-4}  & C & 91 & 56.20\% \\ \hline \multirow{4}{*}{Average driving distance} & 0–3 km & 109 & 67.30\% \\ \cline{2-4}  & 3 km and more & 53 & 32.70\% \\ \hline \multicolumn{4}{c}{Valid} & 162 & 100.00\% \\ \hline \multicolumn{4}{c}{Missing} & 141 \\ \hline \multicolumn{4}{c}{Total} & 304 \\ \hline \multicolumn{4}{c}{Subpopulation} & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample characteristics. addition, other reasons for riding a shared bike include commuting to school. All detailed information on the frequency of responses is shown in Table 2.  
The log-likelihood function (\(-2\) LL) is a statistical measure like total sums of squares in the regression model. If our independent variables have a relationship to the dependent variables, the log-likelihood measure decreases. Table 3 reveals that the final log-likelihood value is 56.162, the measure computed for all independent variables in logistic regression. The difference between these two measures is 98.410 for testing statistical significance. The test is like the F-test. This metric tests whether the improvement is associated with other variables. In other words, Table 3 shows the statistical metrics for evaluating the null model (intercept-only model) and the final model with the intercept and all predictors. We find that the model is statistically significant (Chi-Square of 98.410, \(p<0.001\)).