## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Effect**} & \multicolumn{3}{c}{**Model Fitting Criteria**} & \multicolumn{3}{c}{**Likelihood Ratio Tests**} \\  & \multicolumn{3}{c}{\(-\)**2 Log-Likelihood**} & \multirow{2}{*}{**Chi-Square**} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Sig.**} \\  & & **of Reduced Model** & & & & & \\ \hline Intercept & \(56.162\)\({}^{\text{a}}\) & \(0\) & \(0\) & \(0\) & \(\cdot\) & \\ \hline Status & \(104.040\) & \(47.877\) & \(2\) & \(0.000\) & \(\ast\) \\ \hline Bike-sharing service & \(68.361\) & \(12.199\) & \(4\) & \(0.016\) & \(\ast\) \\ \hline Average driving distance & \(66.311\) & \(10.149\) & \(2\) & \(0.006\) & \(\ast\) \\ \hline \end{tabular} Note. \(\ast\)\(p<0.05\). The Chi-Square statistic is the difference in \(-2\) log-likelihoods between the final model and a reduced model. The reduced model is formed by omitting an effect from the final model. The null hypothesis is that all parameters of that effect are \(0\). \({}^{\text{a}}\) This reduced model is equivalent to the final model because omitting the effect does not increase the degrees of freedom.  
\end{table} 
Table 6: Likelihood ratio tests. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **B** \\ \end{tabular} } & \multicolumn{3}{c}{**Std.**} & \multirow{2}{*}{**Wald**} & \multirow{2}{*}{**df**} & \multirow{2}{*}{**Sig.**} & \multirow{2}{*}{**Exp (B)**} & \multicolumn{3}{c}{**95\% Confidence**} \\  & & & & & & & **Interval for Exp (B)** & \\ \hline \multirow{6}{*}{Relax} & Intercept & \(-0.096\) & \(0.532\) & \(0.033\) & \(1\) & \(0.856\) & & & \\ \cline{2-11}  & [Status = employee-0] & \(1.525\) & \(0.560\) & \(7.413\) & \(1\) & \(0.006\) & \(4.595\) & \(1.533\) & \(13.775\) \\ \cline{2-11}  & [Status = student-1] & \(0\)\({}^{\text{b}}\) & \(\cdot\) & \(\cdot\) & \(0\) & \(\cdot\) & \(\cdot\) & \(\cdot\) & \(\cdot\) \\ \cline{2-11}  & [Bike-sharing companies = A-0] & \(1.238\) & \(0.627\) & \(3.893\) & \(1\) & \(0.048\) & \(3.448\) & \(1.008\) & \(11.788\) \\ \cline{2-11}  & [Bike-sharing companies = B-1] & \(2.634\) & \(1.125\) & \(5.483\) & \(1\) & \(0.019\) & \(13.934\) & \(1.536\) & \(126.388\) \\ \cline{2-11}  & [Bike-sharing companies = C-2] & \(0\)\({}^{\text{b}}\) & \(\cdot\) & \(\cdot\) & \(0\) & \(\cdot\) & \(\cdot\) & \(\cdot\) & \(\cdot\) \\ \cline{2-11}  & [Average driving distance = 0-3 km-1] & \(-1.207\) & \(0.574\) & \(4.432\) & \(1\) & \(0.035\) & \(0.299\) & \(0.097\) & \(0.920\) \\ \cline{2-11}  & [Average driving distance = 3 km and more-2] & \(0\)\({}^{\text{b}}\) & \(\cdot\) & \(\cdot\) & \(0\) & \(\cdot\) & \(\cdot\) & \(\cdot\) & \(\cdot\) \\ \hline Intercept & \(-0.762\) & \(0.600\) & \(1.612\) & \(1\) & \(0.204\) & & & \\ \cline{2-11}  & [Status = employee-0] & \(3.607\) & \(0.618\) & \(34.069\) & \(1\) & \(0.000\) & \(36.856\) & \(10.977\) & \(123.746\) \\ \cline{2-11}  & [Status = student-1] & \(0\)\({}^{\text{b}}\) & \(\cdot\) & \(\cdot\) & \(0\) & \(\cdot\) & \(\cdot\) & \(\cdot\) & \(\cdot\) \\ \cline{2-11} Job & [Bike-sharing companies = A-0] & \(1.362\) & \(0.649\) & \(4.401\) & \(1\) & \(0.036\) & \(3.905\) & \(1.094\) & \(13.942\) \\ \cline{2-11}  & [Bike-sharing companies = B-1] & \(2.188\) & \(1.246\) & \(3.084\) & \(1\) & \(0.079\) & \(8.918\) & \(0.776\) & \(102.506\) \\ \cline{2-11}  & [Bike-sharing companies = C-2] & \(0\)\({}^{\text{b}}\) & \(\cdot\) & \(\cdot\) & \(0\) & \(\cdot\) & \(\cdot\) & \(\cdot\) & \(\cdot\) \\ \cline{2-11}  & [Average driving distance = 0-3 km-1] & \(-1.95\) & \(0.639\) & \(9.302\) & \(1\) & \(0.002\) & \(0.142\) & \(0.041\) & \(0.498\) \\ \cline{2-11}  & [Average driving distance = 3 km and more-2] & \(0\)\({}^{\text{b}}\) & \(\cdot\) & \(\cdot\) & \(0\) & \(\cdot\) & \(\cdot\) & \(\cdot\) & \(\cdot\) \\ \hline \end{tabular} \(\ast\) The reference category is school. \({}^{\text{b}}\) This parameter is set to zero because it is redundant.  
\end{table} 
Table 7: Multinomial logistic regression model \({}^{\text{a}}\).