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b. The Result of Multicollinearity Test
Detection of multicollinearity can be seen, that if the value of Variance Inflation Factor VIF of not more than 10 and the value of tolerance is no less
than 0.1, 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 Results Multicollinearity Test
Model Collinearity Statistics
Tolerance VIF
1 Constant
BOD .766
1.306 BOI
.752 1.329
MO .871
1.148 IO
.819 1.221
a. Dependent Variable: ROA
Source: Secondary Data Output From SPSS 18 Based on table 4.4 above, it can be concluded this research free of
multicollinearity. All independent variables have VIF values less than 10. In addition, each independent variable have a tolerance value is greater than 0.1.
Thus there is no multicollinearity in this regression model.
c. The Result of Autocorrelation Test
How that can be done to detect the presence or absence of autocorrelation
is the Durbin-Watson test DW. Here is the Durbin-Watson test result:
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Table 4.6 Result Autocorrelation Durbin Watson test
MODEL DURBIN WATSON
1 2.011
Source: Secondary Data Output From SPSS 18 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 Du Dw 4-Du there is no autocorrelation. 4 If 4-DL Dw 4 there is any negative autocorrelation.
From the table 4.5 above, note that the value obtained for DW 2.011 which these score will be compared to the table of significant 5. The sample
chosen is 285 and the total of independent variable is 4 k=4, so that in the Durbin Watson table it can be obtain with the value.
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Table 4.7 Durbin Watson Test Bound a = 5
N
k=1 k=2
k=3 k=4
k=5 dL
dU dL
dU dL
dU dL
dU dL
dU
10 0.8791
1.3197 0.6972
1.6413 0.5253
2.0163 0.3760
2.4137 0.2427
2.8217
20 1.2015
1.4107 1.1004
1.5367 0.9976
1.6763 0.8943
1.8283 0.7918
1.9908 .
. .
. .
. .
. .
. .
200
1.7584 1.7785
1.7483 1.7887
1.7382 1.7990
1.7279 1.8094
1.7176 1.8199
Sources: http:www.standford.edu After refers to the table above, the value means including the third criteria,
Du= 1.8094 Dw 2.011 4-Du=2.1906 so it can be concluded that the
regression model free from autocorrelation.
d. The Result of 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.
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Figure 4.8 Source: Secondary Data Output From SPSS 18
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.
3. Coefficient of Determination R
Kategori