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Nazrin Mammadova: The Effect of Investment Decisions on Firms’ Profitability (Empirical
Study on Listed Companies)
Regression analysis was utilized in the study to determine the association between
ROE and important investment decision elements such liquidity, financial leverage,
and asset growth. A correlation value (R) of 0.47 was generated, showing a positive
linear dependency of ROE on the factors of investment decision, including liquidity,
financial leverage, and asset growth.
An adjusted R-squared of 0.195 further revealed that Asset Growth, Financial
Leverage and Liquidity only explain 19.5 percent of the variations in ROE while 81.5
percent is explained by other factors not accounted for in the model.
Table 5: Analysis of variance
Model Sum of Squares df Mean Square F Sig.
a
1 Regression 10.302 3 3.434 8.175 .000
Residual 36.128 86 .420
Total 46.430 89
a. Predictors: (Constant), Liquidity, Asset Growth, Financial Leverage
b. Dependent Variable: Profitability
Source: Results of the analysis of SPSS 16.0
To determine the differences between the means of the dependent and independent
variables and to demonstrate if a relationship exists between the two, the ANOVA
statistics were used. The P-value of 0.000 indicates that ROE and Asset Growth,
Financial Leverage, and Liquidity have a substantial joint association that is
significant at the 5% level of significance, which also demonstrated the significance
of the regression analysis performed at the 95% confidence level.
Table 6: Coefficients of regression
Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) .215 .110 1.949 .055
Asset Growth -.153 .123 -.119 -1.245 .216
Financial Leverage .107 .022 .458 4.795 .000
Liquidity .015 .047 .030 .310 .757
a. Dependent Variable: Profitability
Source: Results of the analysis of SPSS 16.0
The coefficients of determination in table 6 above reveal both a positive relationship
between ROE and other predictor variable of Financial Leverage. When considering
all, the established regression equation is equal to:
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