Classical Test Assumption Analysis and Discussion 1.

57 histogram graph Normal Probability Plot P-P Plot and statistical analysis namely kolmogorov-smirnov test. Figure 4.1 Histogram Graph Source: Output SPSS 20.0 Figure 4.2 Normal P-P Plot Graph Source: Output SPSS 20.0 58 According to the result of normality test using graph analysis namely histogram graph showing a form of bell in histogram graph and Normal Probability Plot P-P Plot showing dots distribution along diagonal line, indicate that regression model has meet the normality assumption. However, graph analysis can emerge different interpretation among reader, so that statistical analysis test is needed to ensure the interpretation mistake for reading the graph. Table 4.11 below will show the result of statistical analysis namely kolmogorov-smirnov test: Table 4.11 Kolmogorov-Smirnov One-Sample Kolmogorov-Smirnov Test Unstandardiz ed Residual N 288 Normal Parameters a,,b Mean .0000000 Std. Deviation 7.12881064 Most Extreme Differences Absolute .078 Positive .063 Negative -.078 Kolmogorov-Smirnov Z 1.327 Asymp. Sig. 2-tailed .059 a. Test distribution is Normal. b. Calculated from data. Source: Output SPSS 20.0 The result of Kolmogorov-Smirnov test on table 4.11 also shows that the value of Kolmogorov-Smirnov 1.327 with the level of significant probability 0059, the value of p 0.05. So the residual data is distributed normally. Therefore, regression model used in this research has met the normality test assumption. 59 b. Multicollinearity Test The aim from multicolinearity test is to test whether the regression model found a correlation among the independent variables. A good regression model should there is no correlation among independent variables. In this research, to detect the presence or absence of multicolinearity can be done by calculating value of variance inflation factor VIF of each independent variable. Table 4.12 Multicolinearity Test Result Coefficients a Model Collinearity Statistics Tolerance VIF 1 Constant ACI .263 3.801 ACE .841 1.189 ACS .238 4.210 ACM .829 1.207 CS .876 1.142 EA .874 1.145 ROA .947 1.056 a. Dependent Variable: AL Source: Output SPSS 20.0 Based on table 4.12 above, the result shows that there is no value of variance inflation factor VIF of each independent variable, which is less than 0.1 or more than 10. So, it can be concluded that there is no multicolinearity. 60 c. Heteroscedasticity Test The aim from heteroscedastisity test is to test whether the regression model occur the variance inequality of the residual from one observation to another observation. A good regression model is homocedastisity or there is no heteroscedastisity. In this research, heteroscedastisity test can be viewed with using the chart Scatterplot between the predicted value of dependent variable ZPRED and residual SRESID. Figure 4.3 Heteroscedasticity Test Result Source: Output SPSS 20.0 From result of figure 4.3 shows that there is no clear pattern, as well as the dots spread above and below zero 0 on the Y axis. So, it can be concluded that there is no heteroscedastisity homocedasticity. 61 d. Autocorrelation Autocorrelation test aims to test whether a regression model there is a correlation between data in variable. A good regression model is a regression that is free from autocorrelation. In this research, autocorrelation test use the Durbin Watson DW test. Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 Constant 107.257 5.724 18.737 .000 ACI 2.687 1.726 .152 1.556 .121 ACE -.496 .587 -.046 -.845 .399 ACS -8.991 1.674 -.550 -5.371 .000 ACM -.231 .089 -.142 -2.596 .010 CS -.169 .195 -.046 -.864 .388 EA -.053 .943 -.003 -.057 .955 ROA -15.271 3.802 -.206 -4.016 .000 a. Dependent Variable: AL Table 4.13 Autocorrelation Test Result Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Durbin- Watson 1 .549 a .302 .284 7.21737 2.019 a. Predictors: Constant, ROA, ACE, ACM, EA, CS, ACI, ACS b. Dependent Variable: AL Regarding Durbin Waston table, value of dL and dU are 1.696 and 2.159. Based on table 4.13 above, the result shows that value of Durbin-Watson DW is 2.019, which mean 1.696 2.019 62 2.159. So it can be concluded that the regression model do not have autocorrelation.

3. Multiple Regression Analysis

Multiple regression analysis used to test the effect of two or more independent variables toward the dependent variable. In this research, Independent variables are ACI number of independent audit committee, ACE number of expertise audit committee, ACS number of audit committee, ACM number of audit committee meetings, Company Size natural logarithm of total asset, External Auditor big 4 and non-big 4 public accountant, Profitability ROA, and dependent variable is audit lag days elapses between January 1 st to date of submitted financial report. Table 4.14 Result of Multiple Regressions Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta 1 Constant 107.257 5.724 ACI 2.687 1.726 .152 ACE -.496 .587 -.046 ACS -8.991 1.674 -.550 ACM -.231 .089 -.142 CS -.169 .195 -.046 EA -.053 .943 -.003 ROA -15.271 3.802 -.206 Source: Output SPSS 20.0 63 The result of multiple regression analysis has been explained in table 4.16. The result of multiple regression analysis with using significance 5 obtained the following equation: Y = 107.257 + 2.687X 1 -0.496X 2 -8.991X 3 -0.231X 4 -0.169X 5 - 0.053X 6 -15.271X 7 +ε From the multiple linear regression equation above, it can be explained for each variable as follows: 1. Constant at 107.257 units stated that if there is no influence or change in audit committee independence, audit committee expertise, audit committee size, audit committee meeting, company size, external auditor, and profitability then the value of firm value will be 107.257. 2. Regression coefficient of Audit Committee Independence X1 marked positive at 2.687. It shows that the influence of Audit Committee Independence on the Audit Lag is positive, which means that if the value or number of Audit Committee Independence is increased by one point, then Audit Lag will increase by 2.687 or on the contrary, with assumption variables X2, X3, X4, X5, X6 and X7 remain or unchanged. 3. Regression coefficient of Audit Committee Expertise X2 marked negative at -0.496. It shows that the influence of Audit Committee Expertise on the Audit Lag is negative or opposite direction, which means that if the value or number of Audit