• Independence – Errors in the conditional distributions are correlated. The explanatory variables are independent of each other, i.e., knowing the value
of one or more of the independent variables does not tell us anything about the others. The Durbin-Watson statistics were used to verify independence.
• Normality – Errors are normally distributed with zero mean and a constant standard deviation. The individual data points of Y (the response variable) for each of the explanatory variables are normally distributed about the line of means (regression line). The Kolmogorov-Smirnov statistics were used to verify normality of error.
• Homoscedasticity – Errors in the conditional distributions should have constant variance. The variance of the data points about the line of means is the same for each explanatory variable. Diagnostic procedures are intended to check how well the assumptions of linear regression are satisfied. The results of examining Independence and Normality are presented in Table 6. All models have Kolmogorov-Smirnov statistics according with significance at a 5% level and the Durbin-Watson statistics value is between 1.5 and 2.5. The results indicate that all developed models satisfy Independence and Normality assumptions.