## 5 Results
### Logistic Regression Model for the Highway Driving  
In the final step, a logistic regression model for CO\({}_{2}\) emissions was developed for the case of the vehicle moving on the highway. As with the previous case, the process began by conducting a univariate analysis of the influence of selected quantitative variables on the dependent variable. The results of the test are presented in Table 11.  
The calculated value of \(p\) in the test for parameter significance was 0.00 for all variables, and therefore all should be taken into account when building the model. In the next step, a multivariate model was developed. The estimated values of the model parameters, along with the results of the Wald tests for their significance, are shown in Table 12.  
For the variable engine speed in RPM, the obtained \(p\)-value is 0.08, indicating that it is not statistically significant. However, for the accepted significance level of \(\alpha=0.05\), the remaining variables were found to be statistically significant (\(p=0.00\)). The model equation takes the following form (13) and (14):  
\[P(environmentally=1|X)=\frac{e^{a}}{1+e^{a}} \tag{13}\]  
where  
\[a=10.77+0.14\cdot V-0.33\cdot\mathrm{L}_{\%}-0.32\cdot P+0.07\cdot M+0.16 \cdot\varphi_{\%} \tag{14}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Anticipated: 1** & **Anticipated: 0** & **Percentage** \\ \hline Observed: 1 & 3894 & 169 & 95.84\% \\ Observed: 0 & 360 & 2525 & 87.52\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Classification matrix of actual and predicted observations for the urban driving model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Asessment** & **Standard Error** & **Wald’s Statistics** & \(p\) \\ \hline Absolute term & 10.77 & 0.75 & 204.84 & 0.00 \\ Vehicle velocity & 0.14 & 0.01 & 590.47 & 0.00 \\ Relative engine load & \(-\)0.33 & 0.01 & 1740.14 & 0.00 \\ Engine speed in RPM & 0.00 & 0.00 & 118.80 & 0.08 \\ Engine power & \(-\)0.32 & 0.07 & 23.23 & 0.00 \\ Torque & 0.07 & 0.02 & 18.80 & 0.00 \\ Relative throttle position & 0.16 & 0.01 & 537.33 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: LR test results for single-factor models while driving on the highway. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Asessment** & **Standard Error** & **Wald’s Statistics** & \(p\) \\ \hline Absolute term & 10.77 & 0.75 & 204.84 & 0.00 \\ Vehicle velocity & 0.14 & 0.01 & 590.47 & 0.00 \\ Relative engine load & \(-\)0.33 & 0.01 & 1740.14 & 0.00 \\ Engine speed in RPM & 0.00 & 0.00 & 118.80 & 0.08 \\ Engine power & \(-\)0.32 & 0.07 & 23.23 & 0.00 \\ Torque & 0.07 & 0.02 & 18.80 & 0.00 \\ Relative throttle position & 0.16 & 0.01 & 537.33 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Estimated values of model parameters while driving on the highway. V represents vehicle velocity, L\({}_{\%}\) represents relative engine load, P represents engine power, M represents torque, and \(\varphi_{\%}\) represents throttle position. In the next step, the goodness of fit of the model was tested based on a comparison of predicted and observed values. For this purpose, the Hosmer-Lemeshow (HS) test was used. The calculated \(p\)-value of the HS test is 0.07 and does not warrant rejection of the null hypothesis, indicating that the model is a good fit to the data. Subsequently, odds ratios were calculated to determine the likelihood that driving with the assumed parameter values would lead to environmentally friendly driving (Table 13). The calculated odds ratios indicate that during highway driving, an increase in driving velocity of 1 km/h results in a 15% increase in the probability that CO\({}_{2}\) emissions will be below 200 g/km. An increase in engine torque by 1 Nm raises this probability by 7%. Additionally, when the relative throttle position changes by 1%, the probability increases by 17%.