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From the excel output, the correlation matrix provides that while the correlation coefficient (R2) is 0.109, the adjusted correlation coefficient is -0.010 and that of the independent P/E and P/B ratios is -0.0102. The result of the correlation coefficient implies that 10.9% of the total variation is explained by the independent variables P/E and P/B while the remaining 89.1% represents the unexplained variation by the regression model. Consequently, the result of the adjusted R2 implies that the model implies that the model presents the best estimate of the degree of the relationship for our variables in the population under study. Consequently, the output presents the standard error as 0.0255 which implies that approximately 95% of the observations should fall within plus/minus 5% of the of the line fitted by the model which presents a close match for the prediction interval.

Lastly, the intercept of 0.067 implies that given a P/B ratio of 0, then we can predict ROE as 0.067.The correlation matrix from the correlation analysis postulate that the correlation of P/B ratio and ROE is -0.324 which indicates that the correlation is weak and negative hence a change in one variable results to a change in the opposite direction of the other variables. Consequently, the correlation between P/E ratio and ROE is -0.226 which implies a weak negative correlation which indicates that a change in one variable to a large extent does not determine the direction of change of the other variable. The correlation of the P/E and P/B ratios is 0.528 which implies a strong positive correlation the change in one variable, either P/E or P/B ratio results to an equal change of the other variable to the same direction hence an increase in P/E results to an increase in P/B and a decrease in P/E results to a decrease in P/B and the inverse is also truest-statistics.