Dependent Variable: BVPS_FSC Method: Least Squares Sample (adjusted): 1999Q2 201

Question

Dependent Variable: BVPS_FSC

Method: Least Squares

Sample (adjusted): 1999Q2 2013Q4

Included observations: 59 after adjustments

Variable           Coefficient       Std. Error        t-Statistic         Prob.

C                     815.2455       111.1638         7.333732        0.0000

CR_FSC         1.217781         0.590490         2.062321         0.0443

CR_IBM         -0.493510        1.902569         -0.259391        0.7964

QTR                -0.001097        0.000153         -7.154896        0.0000

ROA_FSC      0.023430         0.011552         2.028163         0.0478

ROA_IBM      -0.110066        0.058086         -1.894869        0.0638

RT_FSC          -0.066253        0.439835         -0.150630        0.8809

TAT_FSC       -4.044589        2.349533         -1.721443        0.0912

R-squared                    0.688513             Mean dependent var           6.859492

Adjusted R-squared    0.645760             S.D. dependent var           2.015211

S.E. of regression        1.199414            Akaike info criterion           3.327018

Sum squared resid       73.36828            Schwarz criterion                3.608718

Log likelihood            -90.14702            Hannan-Quinn criter.          3.436982

F-statistic                    16.10442            Durbin-Watson stat             0.636046

Prob(F-statistic)          0.000000

1. Discuss in detail the results on the above data.

Explanation / Answer

Consider the given problem here “BVPS_FSC” is the dependent variable and “CR_FSC”, “CR_IBM”, “QTR”, “ROA_FSC”, “ROA_IBM”, “RT_FSC” and “TAT_FSC” are the explanatory variables.

Now, the coefficient of “CR_FSC” is “1.22”, => as “CR_FSC” increases by “1 unit”, => “BVPS_FSC” increases by “1.22” unit. The “p” value is “0.0443=4.43% < 5%”, => this variable is significant at “5%” level of significance but not significant at “1%” level.

The coefficient of “CR_IBM” is “-0.49”, => as “CR_IBM” increases by “10 unit”, => “BVPS_FSC” decreases by “4.9” unit. The “p” value is “0.7964=79.43% > 5%”, => this variable is not significant.

The coefficient of “QTR” is “-0.0011”, => as “QTR” increases by “1000 unit”, => “BVPS_FSC” decreases by “1.1” unit. The “p” value is “0.000 < 1%”, => this variable is significant.

The coefficient of “ROA_FSC” is “0.023”, => as “ROA_FSC” increases by “100 unit”, => “BVPS_FSC” increases by “2.3” unit. The “p” value is “0.0478 = 4.78% < 5%”, => this variable is significant at “5%” level of significance but not significant at “1%” level.

The coefficient of “ROA_IBM” is “-0.110066”, => as “ROA_IBM” increases by “100 unit”, => “BVPS_FSC” decreases by “11” unit. The “p” value is “0.0638 = 6.38% > 5%”, => this variable is not significant at “5%” level of significance but significant at “10%” level.

The coefficient of “RT_FSC” is “-0.0662”, => as “RT_FSC” increases by “100 unit”, => “BVPS_FSC” decreases by “6.62” unit. The “p” value is “0.8809=88.09% > 10%”, => this variable is not significant.   

The coefficient of “TAT_FSC” is “-4.0446”, => as “TAT_FSC” increases by “1 unit”, => “BVPS_FSC” decreases by “4.04” unit. The “p” value is “0.0912=9.12% > 5%”, => this variable is not significant at “5%” level of significance but is significant at “10%” level.

So, here in this model “CR_FSC”, “QTR” and “ROA_FSC” are significant at 5% level of significance. Now, the “p” value of the “F” statistic is close to “0”, => the overall model is also significant. The “R^2” the coefficient of determination is “0.6885=68.85%”, => the model is able explain only “68%” variation in “BVPS_FSC” and rest of the variation remain unexplained, => the value of the “R^” is not quite high but in moderate. So, here we can conclude that model is fitted good.

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