## 5 Research Results and Discussion
### Model for Estimating the Total Daily Number of Visitors and Vehicles in the Shopping Centre Zone
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model** & **Sum of Squares** & **DF** & **Square** & **F** & **Sig.** \\ \hline \multirow{4}{*}{1.} & Regression & 15,894,707.153 & 5 & 3,178,941.431 & 7.202 & 0.275 \\  & Residual & 441,396.561 & 1 & 441,396.561 & & \\  & Total & 16,336,103.714 & 6 & & \\  & Regression & 15,775,831.402 & 4 & 3,943,957.850 & 14.079 & 0.067 \\
2. & Residual & 560,272.313 & 2 & 280,136.156 & & \\  & Total & 16,336,103.714 & 6 & & \\  & Regression & 15,445,891.989 & 3 & 5,148,630.663 & 17.351 & 0.021 \\
3 & Residual & 890,211.725 & 3 & 296,737.242 & & \\  & Total & 16,336,103.714 & 6 & & \\  & Regression & 14,248,130.187 & 2 & 7,124,065.094 & 13.648 & 0.016 \\
4 & Residual & 2,087,973.527 & 4 & 521,993.382 & & \\  & Total & 16,336,103.714 & 6 & & \\  & Regression & 13,085,214.837 & 1 & 13,085,214.837 & 20.126 & 0.006 \\
5 & Residual & 3,250,888.877 & 5 & 650,177.775 & & \\  & Total & 16,336,103.714 & 6 & & \\  & Total & 16,336,103.714 & 6 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANOVA for UBO linear regression models. are shown in Table 8. The Model 1 has the highest values of R, R Square and Adjusted R Square, in which all five independent variables are represented. Based on the results shown in Tables 9 and 10, the same conclusions can be drawn as for the UBO assessment models. Thus, the only statistically significant coefficients (Sig. < 0.05) are the coefficients of model 5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model**} & \multicolumn{2}{c}{**Unstandardized Coefficients**} & \multicolumn{2}{c}{**Standardized Coefficients**} & \multicolumn{2}{c}{**T**} & \multicolumn{1}{c}{**Sig.**} \\ \cline{3-8} \multicolumn{1}{c}{} & \multicolumn{1}{c}{**B**} & \multicolumn{1}{c}{**Std. Error**} & \multicolumn{1}{c}{**Beta**} & \multicolumn{1}{c}{**T**} & \multicolumn{1}{c}{**Sig.**} \\ \hline \multirow{8}{*}{1} & Constant. & 615.674 & 605.575 & & 1.017 & 0.495 \\  & BS & \(-\)0.095 & 0.048 & \(-\)5.710 & \(-\)1.956 & 0.301 \\  & BD & 0.313 & 0.152 & 6.675 & 2.061 & 0.288 \\  & BRPV & \(-\)0.094 & 0.086 & \(-\)2.138 & \(-\)1.096 & 0.471 \\  & UPP & 0.245 & 0.115 & 2.235 & 2.124 & 0.280 \\  & BPM & 0.570 & 1.775 & 0.141 & 0.321 & 0.802 \\ \hline \multirow{8}{*}{2.} & Constant. & 748.404 & 328.958 & & 2.275 & 0.151 \\  & BS & \(-\)0.091 & 0.035 & \(-\)5.500 & \(-\)2.603 & 0.121 \\ \cline{1-1}  & BD & 0.293 & 0.103 & 6.254 & 2.843 & 0.105 \\ \cline{1-1}  & BRPV & \(-\)0.077 & 0.051 & \(-\)1.766 & \(-\)1.514 & 0.269 \\ \cline{1-1}  & UPP & 0.237 & 0.084 & 2.168 & 2.831 & 0.105 \\ \hline \multirow{8}{*}{3} & Constant. & 915 & 370.550 & & 2 472 & 0.090 \\ \cline{1-1}  & BS & 1.071 & 0.039 & \(-\)4.307 & \(-\)1.836 & 0.164 \\ \cline{1-1}  & BD & 0.198 & 0.098 & 4.223 & 2.025 & 0.136 \\ \cline{1-1}  & UPP & 0.125 & 0.046 & 1.139 & 2.699 & 0.074 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Linear model coefficients for UBV estimation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Model**} & \multicolumn{1}{c}{**Independent**} & \multicolumn{1}{c}{**R**} & \multicolumn{1}{c}{**Square**} & \multicolumn{1}{c}{**Adjusted R**} & \multicolumn{1}{c}{**Std. Error of the**} \\  & **Variables/Predictors** & **R** & **Square** & **Estimate** \\ \hline
1. & BPM, BD, UPP, BRPV, BS & 0.984 & 0.969 & 0.812 & 447.353 \\
2. & BD, UPP, BRPV, BS & 0.983 & 0.965 & 0.896 & 332.262 \\
3 & BD, UPP, BS & 0.962 & 0.926 & 0.852 & 397.417 \\
4 & BD, UPP & 0.918 & 0.842 & 0.764 & 501.617 \\
5 & UPP & 0.883 & 0.780 & 0.736 & 529.921 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Linear models for UBV estimation—summary results.