Multiple Regression Analysis Analysis and Discussion 1.
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
64 Committee Expertise is increased by one point, then Audit Lag
will decrease by -0.496 or on the contrary, with assumption variables X1, X3, X4, X5, X6 and X7 remain or unchanged.
4. Regression coefficient of Audit Committee Size X3 marked
negative at -8.991. It shows that the influence of Audit Committee Size on the Audit Lag is negative or opposite
direction, which means that if the value or number of Audit Committee Size is increased by one point, then Audit Lag will
decrease by -8.991 or on the contrary, with assumption variables X1, X2, X4, X5, X6 and X7 remain or unchanged.
5. Regression coefficient of Audit Committee Meeting X4
marked negative at -0.231. It shows that the influences of Audit Committee Meeting on the Audit Lag is negative or
opposite direction, which means that if the value or number of Audit Committee Meeting is increased by one point, then
Audit Lag will decrease by -0.231 or on the contrary, with assumption variables X1, X2, X3, X5, X6 and X7 remain or
unchanged. 6.
Regression coefficient of Company Size X5 marked negative at -0.169. It shows that the influence of Company Size on the
Audit Lag is negative or opposite direction, which means that if the value or number of Company Size is increased by one
point, then Audit Lag will decrease by -0.169 or on the
65 contrary, with assumption variables X1, X2, X3, X4, X6 and
X7 remain or unchanged. 7.
Regression coefficient of External Auditor X6 marked negative at -0.053. It shows that the influence of External
Auditor on the Audit Lag is negative or opposite direction, which means that if the value or number of External Auditor is
increased by one point, then Audit Lag will decrease by -0.053 or on the contrary, with assumption variables X1, X2, X3, X4,
X5 and X7 remain or unchanged. 8.
Regression coefficient of Profitability X7 marked negative at -15.271. It shows that the influence of Profitability on the
Audit Lag is negative or opposite direction, which means that if the value or number of Profitability is increased by one
point, then Audit Lag will decrease by -15.271 or on the contrary, with assumption variables X1, X2, X3, X4, X5 and
X6 remain or unchanged.
66 a.
Simultaneous Significance Testing F- Test Test of F statistic is basically indicates whether independent
variables altogether can influence the dependent variable. In this research, F test done by seeing probability value.
Table 4.15 Result of F-Test
ANOVA
b
Model Sum of
Squares df
Mean Square F
Sig. 1
Regression 6299.146
7 899.878
17.275 .000
a
Residual 14585.323
280 52.090
Total 20884.469
287 a. Predictors: Constant, ROA, ACE, ACM, EA, CS, ACI, ACS
b. Dependent Variable: AL
Based on table 4.16 above, the result of F test shows that
value of F is 17.275 and probability value is 0,000 0,05 sig. F 0.05. This result indicates that the variable of audit lag is
simultaneously influenced by audit committee independence, audit committee expertise, audit size, audit committee meeting, company
size, external auditor, and profitability.
b. Partial Significance Test t- Test
Test of t statistic performed to determine the effect of one independent variable towards the dependent variable. In this
research, t test done by seeing probability value.
67
Table 4.16 Result of t-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
Source: Output SPSS 20.0 Based on table 4.17 above, the result of t test can be
concluded based on probability value value Significance error rate
α=0.05 = Ho is rejected, which will be explained as below: 1.
Audit Committee Independence ACI Based on table 4.17 above, the result of t tests toward variable
of audit committee independence measured by the number of independence members of the Audit Committee shows that
probability value is 0.121 p 0.05. It means that audit committee independence does not have influence tothe audit
lag. Thus, the first hypothesis H1, which states that audit committee independence has influence to audit lag, is not
accepted.
68 2.
Audit Committee Expertise ACE Based on table 4.17 above, the result of t tests toward variable
of audit committee expertise measured by the number of expert members of the Audit Committee shows that
probability value is 0.399 p 0.05. It means that audit committee expertise does not have influence to the audit lag.
Thus, the second hypothesis H2, which states that audit committee expertise has influence to audit lag, is not accepted.
3. Audit Committee Size ACS
Based on table 4.17 above, the result of t tests toward variable of audit committee size measured by the number of members
of the Audit Committee shows that probability value is 0.000 p 0.05. It means that audit committee size has influence to
the audit lag. Thus, the third hypothesis H3, which states that audit committee size has influence to audit lag, is accepted.
4. Audit Committee Meeting ACM
Based on table 4.17 above, the result of t tests toward variable of audit committee meeting measured by the meeting of the
Audit Committee held in a year shows that probability value is 0.010 p 0.05. It means that audit committee meeting has
influence to the audit lag. Thus, the forth hypothesis H4, which states that audit committee meeting has influence to
audit lag, is accepted.
69 5.
Company Size CS Based on table 4.17 above, the result of t test toward variable
of company size natural logarithm of total asset shows that probability value is 0.338 p 0.05. It means that company
size does not have influence to the audit lag. Thus, the fifth hypothesis H5, which states that company size has influence
to audit lag, is not accepted. 6.
External Auditor EA Based on table 4.17 above, the result of t test toward variable
of external auditor represented by dummy variable by classifying the Big Four public accounting firm and non-Big
Four public accounting firm shows that probability value is 0.955 p 0.05. It means that external auditor does not have
influence to the audit lag. Thus, the sixth hypothesis H6, which states that external auditor has influence to audit lag, is
not accepted. 7.
Profitability ROA Based on table 4.17 above, the result of t test toward variable
of profitability measured by ROA shows that probability value is 0.000 p 0.05. It means that profitability has
influence to the audit lag. Thus, the seventh hypothesis H7, which states that profitability has influence to audit lag, is
accepted.
70