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2. The Result of Data Quality Test
a. The Result of Normality Test
Normality of the data was tested using the Kolmogorov-Smirnov Z with a significant level of 0.05. From the Kolmogorov-Smirnov test Z has
done Kolmogorov-Smirnov Z values of 0.996 and significant of 0.275 more than 0.05 means that it can be considered fulfilled normality test Sufren and
Natanael, 2014. Here is the data normality test results:
Table 4.4 Data Normality Test Results
One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual
N 84
Normal Parameters
a,,b
Mean .0000000
Std. Deviation 7.77218021
Most Extreme Differences Absolute
.109 Positive
.109 Negative
-.045 Kolmogorov-Smirnov Z
.996 Asymp. Sig. 2-tailed
.275 a. Test distribution is Normal.
b. Calculated from data.
Source: Data Processed
b. The Result of Multicollinearity Test
Multicollinearity testing in this study conducted by looking at the value of collinearity statistics and the correlation coefficient between independent
variables. The test results shown in table 4.5
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Table 4.5 Multicollinearity Test Results
Coefficients
a
Model Collinearity Statistics
Tolerance VIF
1 indepent
X1 .918
1.090 Manag X2
.950 1.053
GO X3 .961
1.041 SG X4
.942 1.062
Source: Data Processed Multicolinearity test aims to test whether the regression model found a
correlation between independent variables. A good regression model should not happen correlation between independent variables. Multikoloniaritas
occurs when 1 the value of tolerance Tolerance 0.10 and 2 variance inflation factor VIF 10. Based on Table 4.5 indicates VIF of Indepent,
manag, GO and SG is smaller than 10. Meanwhile, tolerancenya value greater than 0.10. This suggests that the independent variables in this study are not
correlated so that the model does not contain multicollinearity Sufren and Natanael, 2014.
c. The Result of Heteroscedasticity Test
Results heteroscedasticity in this study by looking at the scatterplot graph among other residue SDRESID dependent variables with independent
predictive value variable ZPRED. Detection of the presence or absence heterokedastisitas can be seen where Y is the residual value and the value of X
is the predicted value. The scatterplot graph can be seen from Figure 4.1
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Based Scatterplot image above can be concluded that there is no clear pattern, as well as the points spread. Thus, the analysis model
can be concluded not happen heterocedastisity Sufren and Natanael, 2014.
d. The Result of Autocorrelation Test
Kategori