3.3.2. Analysis of Multiple Regression Model

Data analysis was conducted in order to validate the ANN results by comparing them with the multiple regression results. The correlation coefficient and determination coefficient of this study's fitted multiple regression were 0.864 and 0.747, respectively. This means that about 74% of the dependent variable's variance is determined according to the model's independent variables. Information regarding the mentioned coefficients and the model analysis results is illustrated in Tables 5 and 6, respectively.





In Table 6, B and β stand for unstandardized coefficients and standardized coefficients, respectively. Although it is easier to write the multiple regression model's equation using unstandardized coefficients, using standardized coefficients enables researchers to compare variables more easily. In other words, a higher value of the coefficient means that the variable can predict the outcome more effectively. According to the results, "Risk management", "Plans", "Project schedule", "Relationship among project's parties" and "Conflicts" are the most important factors.

In this study, an artificial neural network model for road construction projects was used in order to improve the prediction of the earned value. Moreover, a multiple regression model was used to validate the ANN results. The ANN and multiple regression models' calculated mean squared errors and the real values of projects are illustrated in Table 7. As it is easily seen, both the ANN model and the multiple regression model possess low errors. Moreover, the ANN model not only had the lowest error, but also possessed the most effective prediction coefficient.

**Table 7.** Comparison of the ANN and multiple regression models.

