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Effect of Corporate Social Responsibility, Independent Commissioner and Profitability on Tax Avoidance IV.

Source:

Based on the value of One Sample Kolmogorov Smirnov above, the significance value of the Asymp Sig (2tailed) shows a result of 0.881 or 88.1% which indicates that the data is normally distributed, this is because the value is greater than 0.05 or 5%.

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Multicollinearity

From the test results above, it shows that the independent variables in this study have a tolerance value of <0.1 and a VIF value of <10, meaning that the regression model used does not occur multicollinearity. Heteroscedasticity

Effect of Corporate Social Responsibility, Independent Commissioner and Profitability on Tax Avoidance

Based on the test results above, it indicates that all independent variables (independent variables) have a significance value greater than 0.05 or 5%, which indicates that the regression equation is free from heteroscedasticity problems.

Autocorrelation Test Results

Source: Processed secondary data, 2022

Based on the test results above, Durbin Watson is worth 1.847, Dl value is 1.3448, Du value is 1.7201, Dw value is 1.847. And it shows that there is no autocorrelation, because DU < DW < 4-DU = 1,720<1,847<2,279

FTest Results

Source: Processed secondary data, 2022

Based on the results of the F test contained in the results of the multiple linear analysis table, it shows that the calculated F has a value of 3.542 > 2.59 F table with a significant value of 0.014 which means the significant value is less than 0.05 or 5%. This indicates that the independent variables, namely corporate social responsibility, capital intensity, independent commissioners, profitability have a simultaneous or joint influence on the dependent variable, namely tax avoidance.

T test results

Based on the table above, it is known that together the variables of Corporate Social Responsibility, Capital Intensity, Independent Commissioner and profitability affect tax avoidance. Then the hypothesis is accepted. This means that regression can be used to predict the dependent variable.

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