03. Findings - Adhwa - Findings Regression Statistics DV =...

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Findings: Regression Statistics DV = ROA DV = ROE DV = EPS DV = DPS Multiple R 92.5% 90.2% 86.9% 56.5% R Square 85.5% 81.4% 75.4% 31.9% Adjusted R Square 42.1% 25.7% 1.8% -172.3% Standard Error 1.4% 2.2% 7.0% 4.0% Observations 5 5 5 5 1. High Coefficient of multiple determination for multiple regression For test held on DV (Dependent Variables) 1, 2, 3, these models do explain all the variability of the response data around its mean evidence by high R square values. In other words, it can be conclude that in agriculture industry, tax incentive would have a significance influences towards company’s performances. From relationship point of view, for each DV it could be explained as follows: i. A test performed on Return on Assets (ROA): ROA is defined as an indicator of how profitable a company is relative to its total assets. ROA gives an idea as to how efficient management is at using its assets to generate earnings. Calculated by dividing a company's annual earnings by its total assets, ROA is displayed as a percentage. Sometimes this is referred to as "return on investment". In this particular test, it is proven that the more tax incentives applied by a company, the higher ROA would be simply because the same amount of assets would be able to generate higher profit due to the tax savings that has been maximize. Although this would not always be the case, considering that the amount of cash through tax savings activities would also being added to the total assets, hence could result to the reduction of ROA to certain extent. According to Louis H. Amato et all (2006) sensitivity of the sign and significance level for return on assets related to the inclusion or exclusion of industry effects and to linear and cubic specification of firm size indicates that there is a complex relationship between firm size, profitability, and industry in explaining variations in ‘charitable giving’ (of which in this case it can be substitute with Tax Incentives as an independent variables). Hence, ROA would be the ideal ratio to be dealt with due to the fact that the assets of the company are comprised of both debt and equity. Both of these types of financing are used to fund the operations of the company. The ROA figure gives investors an idea of how effectively the company is converting the money it has to invest into net income. The higher the ROA number, the better, because the company is earning more money on less investment. For example, if one company has a net income of $1 million and total assets of $5 million, its ROA is 20%; however, if another company earns the same amount but has total assets of $10 million, it has an ROA of 10%. In this test also, we could established a relation that, for those companies which applies tax incentives, more profit could be generate, without having to invest huge capital expenditure (through asset purchasing) to generate more profits.
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