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Based on the results obtained from the regression coefficients above, a regression equation can be made as follows: Table 6. Equation of Multiple Linear Regression Analysis = The analysis of multiple regression equations aims to explain the relationship between the influence of independent variables with the dependent variable in a study. Following below are the equations of the multiple linear regression model in this study (table 6):

Table 6 Based on the results obtained from the regression coefficients above, a regression equation can be made as follows: Table 6. Equation of Multiple Linear Regression Analysis = The analysis of multiple regression equations aims to explain the relationship between the influence of independent variables with the dependent variable in a study. Following below are the equations of the multiple linear regression model in this study (table 6):

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Table 1. Multicollinearity Test Based on the test table 1, each variable has a tolerance value of more than 0.10 and a VIF value of less than 10 in all variables used in the study. It is said that there is no multicollinearity among the independent variables studied.
Table 1. Multicollinearity Test Based on the test table 1, each variable has a tolerance value of more than 0.10 and a VIF value of less than 10 in all variables used in the study. It is said that there is no multicollinearity among the independent variables studied.
Table 2. Heteroscedasticity Test The table 2 above shows that the independent variables X1, X2, and X3 have significance values above 0.05. So it can be concluded that this regression model is free from heteroscedasticity so that it can proceed to further testing. The heteroscedasticity test aims to test whether, in the regression model, there is an unequal variance from the residuals of one observation to another (Ghozali, 2016: 134). In this study, researchers used the glacier test. Glejser test can be seen from the significance value. If the significance is greater than 5% (0.05), it can be concluded that the regression model does not contain heteroscedasticity.
Table 2. Heteroscedasticity Test The table 2 above shows that the independent variables X1, X2, and X3 have significance values above 0.05. So it can be concluded that this regression model is free from heteroscedasticity so that it can proceed to further testing. The heteroscedasticity test aims to test whether, in the regression model, there is an unequal variance from the residuals of one observation to another (Ghozali, 2016: 134). In this study, researchers used the glacier test. Glejser test can be seen from the significance value. If the significance is greater than 5% (0.05), it can be concluded that the regression model does not contain heteroscedasticity.
Table 5. T Statistics Test Based on the T-Test results table 5, it appears that the level of significance for the variable time budget pressure (X1) is 0.027, smaller than 0.05. it can be concluded that the time budget pressure variable partially influences the audit quality decrease variable (Y). As for the variable task complexity (X2) and professional commitment (X3), each has a significant level of 0.104 and 0.363, which means greater than 0.05. This shows that the task complexity (X2) and professional commitment (X3) variables have no partial effect on the audit quality reduction variable (Y). 4.3.3 TStatistics Test
Table 5. T Statistics Test Based on the T-Test results table 5, it appears that the level of significance for the variable time budget pressure (X1) is 0.027, smaller than 0.05. it can be concluded that the time budget pressure variable partially influences the audit quality decrease variable (Y). As for the variable task complexity (X2) and professional commitment (X3), each has a significant level of 0.104 and 0.363, which means greater than 0.05. This shows that the task complexity (X2) and professional commitment (X3) variables have no partial effect on the audit quality reduction variable (Y). 4.3.3 TStatistics Test
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