## 5 Research Results and Discussion
### Model for Estimating the Total Daily Number of Visitors and Vehicles in the Shopping Centre Zone  
The formation of a model to estimate the total number of visitors, i.e., the total number of vehicles attracted by the attraction of the shopping centre zone, is based on a linear regression analysis. Models based on linear regression start from the following hypotheses:  
**Hypothesis 1. ** (Null Hypothesis) _There is no linear dependence with the given dependent variable between the independent variable values figuring in the set regression equation. The null hypothesis is accepted if the probability for the accuracy of the null hypothesis (p-value) is higher than the given significance threshold (\(\alpha\)), otherwise the null hypothesis is rejected._  
**Hypothesis 2. ** (Alternative Hypothesis) _An assertion contrary to that defined in the null hypothesis. An alternative hypothesis is not rejected when the probability for the accuracy of the null hypothesis (p-value) is less than the given significance threshold (\(\alpha\))._  
Normal values of the significance threshold \(\alpha\) range in the interval \(0.15\geq\alpha\geq 0.05\). For the formation of the given model, the adopted value of the significance threshold is \(\alpha\) = 0.05, i.e., a 95% confidence interval is accepted. Initial model limitations:  
* the model refers to visits to the shopping centre by a passenger car,
* the model can be applied to towns between 56,125 and 210,307 inhabitants,
* the model can be applied to towns between 18,862 and 77,717 households in size,
* the model can be applied to towns with a total number between 20,829 and 76,769 registered passenger vehicles,
* the model can be applied in cases where the total sales area of the shopping centre has a value between 4000 and 30,200 m\({}^{2}\),
* the model can be applied in cases where the total number of parking spaces of the shopping centre has a value between 215 and 970. Linear regression analysis was performed in IBM SPSS Statistics 22 software environment. Two regression models were formed:  
1. Model for estimating the total daily number of visitors who visit the shopping centre using vehicles,
2. Model for estimating the total daily number of vehicles in the shopping centre zone accessing the parking facility. Out of a total of eight observed locations, the characteristics determined on seven facilities were used as the basis for the formation of the model, one randomly selected location was used to test the model. Selection of independent variables was performed by the Backward Elimination algorithm, which starts by including all variables in the equation/model, and then their successive removal is performed. The variable with the lowest partial correlation with the dependent variable is considered as the first to be eliminated if it meets the elimination criterion, which by default is set to a probability F value equal to 0.1. If the F probability obtained is higher than the set criterion, the variable is removed. The next variable in the equation with the least partial correlation with the dependent variable is considered for removal. The procedure is stopped when there are no variables in the equation that meet the removal criteria. In order to simplify the work, the five observed independent variables are marked in the regression analysis as follows:  
* Population--BS
* Number of households--BD
* Number of registered passenger vehicles--BRPV;
* Total sales area--UPP;
* Parking spaces--BPM  
The two dependent variables have the following labels:
* Total daily number of persons in vehicles on arrival (drivers and passengers together) visiting the shopping centre using vehicles--UBO;
* The total daily number of vehicles in the shopping centre zone accessing the parking lot of the facility--UBV.