3.2. Regression analysis
The regression analysis aims to fulfill the following fundamental assumptions for errors [11]:
x Normal distribution.
x Constant variance i.e. homoscedasticity
x The error term has zero mean.
x Uncorrelated errors.
x Approximate linear relationship between the response and the regressors.
The model is analyzed to determine the conformity of these assumptions to avoid any model inadequacies [11]. The first two assumptions of normality and homoscedasticity are conformed by generating the estimated effects on Table 2 and Table 3 of RC and RH.