53
Figure 4.2
Source : Secondary Data Output From SPSS 21
Histogram graph showing a normal distribution pattern because the graph does not deviate to the left or off to the right.
b. Multicollinearity Test
Multicollinearity test aims to test whether the regression model found a correlation between the independent variables Ghozali,
2013:105. A good regression model should not have correlation between the independent variables. To detect the presence or absence of
multicollinearity in the regression model can be seen from the value of tolerance and the variance inflation factor opponent VIF.
54
Multicollinearity views of the tolerance value 0.10 or VIF 10 it can be said to be free of multicollinearity.
VIF values and tolerance of other research variables can be seen from the following table.
Table 4.5
Coefficients
a
Model Collinearity Statistics
Tolerance VIF
1 Constant
LNPAD .605
1.653 LNDAU
.521 1.918
LNDAK .828
1.208 a. Dependent Variable: LNBD
Source : Secondary Data Output From SPSS 21
Based on table 4.5 above, it can be concluded this study is multicollinearity symptom free. All independent variables have VIF values
less than 10. In addition, each independent variable tolerance value is greater than 0.1. Thus there is no multicollinearity in this regression
model.
c. Autocorrelation Test
According Ghozali 2013:111, for detecting the presence or absence of autocorrelation can use the Durbin-Watson test DW test.
Here is the Durbin-Watson test result.
55
Table 4.6
Source : Secondary Data Output From SPSS 21
The criteria for the assessment of the autocorrelation are:
1 If 0 Dw DL there is any positive autocorrelation. 2 If DL Dw Du or 4-Du D 4-DL uncertain conclusion.
3 If 0 Dw DL or Du Dw 4-Du there is no autocorrelation. 4 If 4-DL Dw 4 there is any negative autocorrelation.
From the table above, note that the value obtained for DW 1.869, which means including the third criteria, Du=1,7119 Dw 1.869 4-Du=
2,2881 so we can conclude that there is no autocorrelation in this regression model too.
d. Heteroscedasticity Test
This test is done by observing certain chart patterns scatterplot, where if there is a point-point spread above and below the 0 on the Y axis
and does not constitute that it does not happen heteroscedasticity Ghozali,
2013 : 139. Scatterplot graphs can be seen in figure below.
Model Summary
b
Model Durbin-Watson
1 1.869
a. Predictors: Constant, LNDAK, LNPAD, LNDAU b. Dependent Variable: LNBD
56
Figure 4.3
Source : Secondary Data Output From SPSS 21
From figure 4.3 shown 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 heterocedastisity.
2. The Result of Multiple Regresion Testing
