## 5 Results and Discussion
### Model of Multiple Linear Regression (MLR)  
The multiple linear regression model, as one of the most commonly used statistical models, was also created in the IBM SPSS technology. Initially, in the regression analysis, the dimensionality of the input space was represented by 17 previously analyzed independent variables. Unlike MLP, the output space in the regression model consists of only one dependent variable, so that predicting the value of the average data throughput at the cell level and at the user level along the observed segment of the motorway requires two independent models.  
By regression analysis, the variable \(RRC\)._CommReq_Att_ was excluded from both models due to the existence of strong multicollinearity with the variable \(RRC\)._CommReq_._Succ_, which means that in this case it represents a redundancy. Table 6 shows the results of the regression analysis that allow the value of the observed target dependent variable (\(Y_{1}\)_and_\(Y_{2}\)) to be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**QCI** & **Priority** & **Resource Type** & **Delay Budget (ms)** & **PELR** & **Example of Service** \\ \hline
1 & 2 & GBR & 100 & \(10^{-2}\) & Conversational voice \\
2 & 4 & GBR & 150 & \(10^{-3}\) & Conversational video (live streaming) \\
3 & 3 & GBR & 50 & \(10^{-3}\) & Real-time gaming \\
4 & 5 & GBR & 300 & \(10^{-6}\) & Non conversational video (buffered streaming) \\
5 & 1 & Non-GBR & 100 & \(10^{-6}\) & IMS signaling \\
6 & 6 & Non-GBR & 300 & \(10^{-6}\) & Video (buffered streaming), TCP-based (e.g., www, e-mail, chat, FTP, P2P file sharing, progressive video, etc.) \\
7 & 7 & Non-GBR & 100 & \(10^{-3}\) & Voice, video, interactive gaming \\
8 & 8 & Non-GBR & 300 & \(10^{-6}\) & Video (buffered streaming), TCP-based (e.g., www, e-mail, chat, FTP, P2P file sharing, progressive video, etc.) \\
9 & 9 & Non-GBR & 300 & \(10^{-6}\) & Video (buffered streaming), TCP-based (e.g., www, e-mail, chat, FTP, P2P file sharing, progressive video, etc.) \\ \hline \hline \end{tabular}
\end{table}
Table 5: QCI values and corresponding parameters in LTE network [45].  
calculated based on a linear combination of inputs--independent variables denoted by \(X_{1}\), \(X_{2}\), \(X_{3}\),..., \(X_{16}\). Of particular interest is the column showing the nonstandardized coefficients (\(B\)) that appear in the multiple linear model. Coefficients B represent the estimated model parameters from the data using the method of least squares. Based on the values shown in the column marked with Sig., it can be concluded that all coefficients, in both models, including the constant have a value less than 0.05, which means that they are statistically significant and need to be included in the models.  
Based on the coefficients shown in Table 6, the following regression model was formed, which represents a linear dependence of the value of the variable _Cell Downlink Average Throughput_ of the 16 research variables:  
\[\begin{split} Y_{1}=-922.443-416.156X_{1}+1359.353X_{2}+0.428X_{3}- 22.248X_{4}-289.941X_{5}+748.020X_{6}+1043.475X_{7}-0.157X_{8}\\ -0.046X_{9}+0.033X_{10}+0.075X_{11}+0.016X_{12}-0.025X_{13}+0.004X_{14}-0.0 05X_{15}+0.003X_{16}\end{split} \tag{4}\]  
The value of the determination coefficient of this model is R\({}^{2}\) = 0.541.  
Another regression model defines the linear connection of the variable _Average User Downlink Throughput_ and the observed set of the independent variables and can be written in the following form:  
\[\begin{split} Y_{2}=-11.194-0.941X_{1}+3.261X_{2}+0.001X_{3}-0.1 03X_{4}+0.202X_{5}+0.046X_{6}+1.031X_{7}-7.331\cdot 10^{-5}X_{8}+7.704\cdot 10^{-5}X_{9 }\\ +4.820\cdot 10^{-5}X_{10}+1.603\cdot 10^{-5}X_{11}-4.992\cdot 10^{-5}X_{1 2}-1.762\cdot 10^{-5}X_{13}+1.695\cdot 10^{-6}X_{14}-8.159\cdot 10^{-6}X_{15}+1.049 \cdot 10^{-5}X_{16}\end{split} \tag{5}\]  
the value of the determination coefficient of this model is R\({}^{2}\) = 0.543.