Dependent Variable: BVPS_FSC Method: Least Squares Date: 07/25/18 Time: 12:06 Sa

Question

Dependent Variable: BVPS_FSC                                          

Method: Least Squares                                               

Date: 07/25/18   Time: 12:06                                     

Sample (adjusted): 4/01/1998 4/01/2013                                            

Included observations: 15 after adjustments                                       

Variable           Coefficient       Std. Error        t-Statistic         Prob.

C                     3.316771        5.621129         0.590054         0.5714

CET_FSC       0.013773         0.021733         0.633729         0.5439

CR_FSC         0.489317         3.034456         0.161254         0.8759

CTR_FSC       0.008914         0.008949         0.996106         0.3484

ROA_FSC      -2.286163        1.433001         -1.595368        0.1493

ROE_FSC       0.759472         0.474621         1.600166         0.1482

ROI_FSC        0.261457         0.198688         1.315919         0.2247

R-squared                     0.360769            Mean dependent var           6.687333

Adjusted R-squared    -0.118654           S.D. dependent var             1.987921

S.E. of regression        2.102553            Akaike info criterion           4.628907

Sum squared resid       35.36584            Schwarz criterion                4.959330

Log likelihood           -27.71680             Hannan-Quinn criter.          4.625387

F-statistic                     0.752506             Durbin-Watson stat             0.637955

Prob(F-statistic)          0.625229                                 

1. discuss in detail the above data

Explanation / Answer

Answer:

From given regression output, it is observed that the dependent variable is given as BVPS_FSC. The least square multiple linear regression model is used for the prediction of dependent variable. In this regression model, there are total six independent variables. The value for R squared or coefficient of determination is given as 0.36, which means about 36% of the variation in the dependent variable is explained by the independent variables. The F test statistic for this regression model is given as 0.7525 with the P-value as 0.6252. This P-value is greater than alpha value 0.05 or 5% level of significance, and therefore we conclude that this regression model is not statistically significant. This means we could not use this regression model for future use.

Now, let us see the significance of the coefficients of the independent variables and intercept in the given multiple linear regression model. The t-test for regression coefficient of intercept gives the P-value as 0.5714 which is greater than alpha value 0.05, so we conclude that the intercept for this regression model is not a statistically significant. The regression coefficients in the given regression outputs shows the P-values greater than the alpha value 0.05, therefore we conclude that all independent variables are not statistically significant. The post hoc Durbin Watson test also indicate that the given regression model is not statistically significant.

Overall, we concluded that the given multiple linear regression model is not useful for the future prediction of dependent variable BVPS_FSC based on the given six independent variables.

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