The statistical informational data was from LUVLE course website: https://luvle.lancs.ac.uk/Acfin/703.nfs.

From Graph 2 above, the researcher found out that there were two Firms who makes a remarkable inflation in terms of ratio analysis. The Firm 6 got the highest inflation of RTA, ITTA, Assets turnover, Profit margin and Equity Multiplier which range above 20 and followed by Firm 21, range almost 20. There were three Firms who were almost on range 10. The three Firms were 9, 12, and 24.

Summary of the graphs were scrutinized by the researcher. It was found out that only Firm 6 got high inflation. The Firm 21 got a high Analysis of ratio but slightly lows in terms of R & D. This implies that the only Firm 6 perform well in the stock market place and it has good inflation elasticity. It was notable that some Firms were also trying their best to be on top such as Firm 9, Firm 2, Firm 8, Firm 5, and Firm 4. This can be seen in their inflation elasticity variables.

The Analysis of Variance was used to determine if the R&D to Total Assets, Intangibles to Total Assets, Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have an effect on the Return on Equity. The null hypothesis is that the Assets turnover, Profit margin, Equity Multiplier, R&D to Total Assets, Intangibles to Total Assets, and Return on total assets have no effect on the Equity Valuation.

Thus, Ho: μ1 = μ2 = μ3= μ4= μ5= μ6, or Ho: All independent variables have no significant effect on the equity valuation. The alternative is that not all independent variables have a significant effect on the return on equity. Let’s assume that there should be at most five percent chance of erroneously rejecting a true Ho. Thus we specify a level of significance of 0.05. We used F-distribution and an Analysis of variance (ANOVA)test , and next step is to define the rejection or critical region. The dfnum value is k-1, or 6 and the dfden value is T-k, or 18. So with α = 0.05, the critical value of F in this analysis of variance test is F0.05 (6,18) = 2.66. The decision Rule is Reject Ho in favor of Ha if the value of the computed F is greater than the value of tabulated F. Otherwise, do not reject Ho. The next step is to compute the test statistic. Find the computed F, by dividing the mean sum of square of regression value by the mean sum of square of residual value. The final step now is to make the statistical decision. Since computed F (F c) is greater than Tabulated F (Ft), Ho is rejected and thus, not all the independent variables of this study have a significant effect on the return on equity. The result of the “Analysis of Variance” (ANOVA) shows that the computed F (13.656) is greater than the tabular values of F-statistics at 0.05 degree of freedom (2.66). Hence, Ho must be rejected.

The Regression results are as follows: the unbiased estimator of the variance of the error in the multiple regression model is equal to .010. There is small value of MSE denominator than the MSE numerator (.131) so the estimator is a good fit of the regression. Standard error of estimate is equal to .09777. Multiple coefficient of determination is .820 (R2) and an adjusted multiple coefficient of determination is equal to .760 (R2) showed that the data produced a good predictions. This was stated because the adjusted R^2 is closer to the unadjusted R^2.

From Graph 2 above, the researcher found out that there were two Firms who makes a remarkable inflation in terms of ratio analysis. The Firm 6 got the highest inflation of RTA, ITTA, Assets turnover, Profit margin and Equity Multiplier which range above 20 and followed by Firm 21, range almost 20. There were three Firms who were almost on range 10. The three Firms were 9, 12, and 24.

Summary of the graphs were scrutinized by the researcher. It was found out that only Firm 6 got high inflation. The Firm 21 got a high Analysis of ratio but slightly lows in terms of R & D. This implies that the only Firm 6 perform well in the stock market place and it has good inflation elasticity. It was notable that some Firms were also trying their best to be on top such as Firm 9, Firm 2, Firm 8, Firm 5, and Firm 4. This can be seen in their inflation elasticity variables.

The Analysis of Variance was used to determine if the R&D to Total Assets, Intangibles to Total Assets, Assets turnover, Profit margin, Equity Multiplier, and Return on total assets have an effect on the Return on Equity. The null hypothesis is that the Assets turnover, Profit margin, Equity Multiplier, R&D to Total Assets, Intangibles to Total Assets, and Return on total assets have no effect on the Equity Valuation.

Thus, Ho: μ1 = μ2 = μ3= μ4= μ5= μ6, or Ho: All independent variables have no significant effect on the equity valuation. The alternative is that not all independent variables have a significant effect on the return on equity. Let’s assume that there should be at most five percent chance of erroneously rejecting a true Ho. Thus we specify a level of significance of 0.05. We used F-distribution and an Analysis of variance (ANOVA)test , and next step is to define the rejection or critical region. The dfnum value is k-1, or 6 and the dfden value is T-k, or 18. So with α = 0.05, the critical value of F in this analysis of variance test is F0.05 (6,18) = 2.66. The decision Rule is Reject Ho in favor of Ha if the value of the computed F is greater than the value of tabulated F. Otherwise, do not reject Ho. The next step is to compute the test statistic. Find the computed F, by dividing the mean sum of square of regression value by the mean sum of square of residual value. The final step now is to make the statistical decision. Since computed F (F c) is greater than Tabulated F (Ft), Ho is rejected and thus, not all the independent variables of this study have a significant effect on the return on equity. The result of the “Analysis of Variance” (ANOVA) shows that the computed F (13.656) is greater than the tabular values of F-statistics at 0.05 degree of freedom (2.66). Hence, Ho must be rejected.

The Regression results are as follows: the unbiased estimator of the variance of the error in the multiple regression model is equal to .010. There is small value of MSE denominator than the MSE numerator (.131) so the estimator is a good fit of the regression. Standard error of estimate is equal to .09777. Multiple coefficient of determination is .820 (R2) and an adjusted multiple coefficient of determination is equal to .760 (R2) showed that the data produced a good predictions. This was stated because the adjusted R^2 is closer to the unadjusted R^2.

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