Classical Assumption Test Analysis and Discussion
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The analysis of the data tolerance value shows there is no independent variable, which has a tolerance value less than 0.10 that
means there is no correlation among independent variables that value more than 95 percent. On the other hand VIF shows similar things that there is
no independent variable that has a VIF value more than 10, thus, it can be concluded that there is no multicolinearity among independent variables in
regression model and feasible to use. b. Heteroscesdastisity Test
Figure 4.1 Heteroscesdastisity Test
Source: Processed Primary data by SPSS 20
According Imam Ghozali 2005:105 multiple linear regression free of heterocedasticity as follows:
1 There is no clear pattern
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2 Point spread above and below zero on the Y axis Heteroscesdasticity test is aimed to examine whether in the model
occur any residual variance in certain monitoring period to other monitoring period. If the characteristic is fulfilled, it means that the factors
of intruder variation toward the data have the characteristic of heteroscedasticity.
A good
model is
homocesdastisity, not
heteroscedasticity. From the Scatter plot diagram in figure 4.1 above, it can be seen
that the dots are spread biased. This shows that there is a heteroscesdastisity problem.
Therefore the Glejser test will be conducted to make sure this content is free from heteroscesdastisity problem. And the result from
Glejser test is shown in table below. Table 4.68
Glejser Test
Coefficients
a
Model Unstandardized
Coefficients Standardized
Coefficients T
Sig. B
Std. Error Beta
1 Constant
.525 1.428
.368 .714
CAE .087
.062 .338
1.414 .161
BAW .019
.104 .023
.179 .858
BAS .069
.074 .191
.922 .359
BPT -.092
.060 -.387 -1.521
.132 a. Dependent Variable: ABS_RES
Source: Processed Primary data by SPSS 20
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From the result of the Glejser test, the significancy result from that testing is more then 0.05 so, it is can be concluded there is no
heteroscesdastisity problem. c. Normality Test
Figure 4.2 Normality Test Result
Source: Processed Primary Data by SPSS 20
Normality data test is aimed to know whether the data obtained from the research activities have a normal distribution or not. Good data is
to be considered, as data that has a normal distribution. One of the ways to see whether the data in this research are normal or not, is by applying the
P-P Plot graph. When the plots in the graph are distributed along the diagonal line, it can be said that the data has a normal distribution.
Based on figure 4.2 this research has done normality data
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distribution test. The result acquired from SPSS 20 statistic software. From the P-P Plots diagram above, it can be seen that the plots are distributed
along the diagonal line. Thus, it can be concluded that the data used in this research has a normal distribution.
Figure 4.3 Chart
Source: Processed Primary data by SPSS 20
Based on chart 4.3 above, the Histogram Graphic shows normal distribution. Because, these images form the normal curve and most of the
bars rods under the curve, then the variable is normally distributed.
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