The ANOVA analysis of shows that the model is statistically significant. The p-value for the F-statistic is nearly 0. So, the null hypothesis that all the coefficients of the model are simultaneously equal to zero is rejected with nearly 100% confidence. The explanatory power of the model is 0.357 which implies that the model explains the variations in the dependent variable to the extent of 35.7%. The model appears to be free from multicollinearity despite high correlations because half the variables are statistically significant and the explanatory power of the model is low.
The constant term of the model is 0.357 but is not statistically different from zero because the p-value of the t-statistic is more than even 0.1. So, the null hypothesis that the coefficient is statistically different from zero cannot be rejected. The coefficient of MS is 0.813 and the p-value of the t-statistic is 0.16. Thus, the null hypothesis can be rejected at 5% level of significance. The coefficient of CR3 on the other hand is -0.143 but it is not statistically significantly different from zero. However, the coefficients of total deposits and GDP are both statistically significant. The coefficient of total deposits is significant at 1% level of significance whereas the coefficient of GDP is significant at 10% level of significance.
The model clearly points that for a unit increase in market size of the bank, the ROA is expected to increase by 0.813 ceteris paribus. Similarly, all other things held constant, if the deposit increase by a unit, the ROA is expected to increase by 7.428 x 10-11. Likewise, if all the other variables are held static, or a unit increase in GDP, the ROA of the banks is likely to go down by a fraction of 1.954 x 10-13. While the coefficient of CPI in the model developed is negative, it is statistically not significant i.e. it is not statistically different from zero. So, its effect on the dependent variable is negligible.