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According to the table 4.40 the value of the Kolmogorov - Smirnov test was 0,956 so it can be seen that the value
unstandardized residual value Asymp. Sig 0,05 and this means that data is distributed normally.
2. Test Results Multicollinearity
Multicollinearity test aims to test whether the regression model found a correlation between the independent variables.
Good model should not happen correlation between independent variables and not orthogonal or correlation values between the
members of the independent variables equal to zero. Can also be seen from the value of tolerance and Variance Inflation Factor
VIF, tolerance values above magnitude 0.1 and VIF values below 10 indicate that there is no multicollinearity in the independent
variable Ghozali, 2011:95. VIF test results from the regression model can be seen in the following table:
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Tabel 4.41 Test Results Multicollinearity
Coefficients
a
Model Collinearity Statistics
Tolerance VIF
1 Constant
x1 .619
1.614 x2
.805 1.243
x3 .636
1.572 a. Dependent Variable: Y
Source: SPSS output the results of the primary data that have been processed, 2016
Based on the results of test results table 4.41 Variance Inflation Factor VIF of each independent variable has a VIF 10
and Tolerance 0,1 i.e for service quality variable X1 of 1,614 and 0,619, for the variable sales promotion X2 1,243 and 0,619
and for customer satisfaction variables X3 1,572 and 0,636. It can be stated linear regression models are not multicollinearity between
the dependent variable with other independent variables that can be used in this study.
3. Test Results Heteroskedastity
Heteroskedastity test aims to test whether the regression occurred inequality residual variance from one observation to
another. Heteroskedastity shows that variation of the variable is not the same for all observations. In heteroskedastity errors that occur
are not random but show the systematic relationship in accordance with the amount of one or more variables.
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Test heteroskedastity in graph Scatterplot detection of the presence or absence of heteroskedasticity can be done by looking
whether there is a specific pattern on a scatterplot graph between SREID and ZPREID wherein Y is the Y axis is predictable, and the
X axis is the residual prediction Y - Y in fact who have been in student zed Ghozali, 2011:125-126.
Based on the results of data processing, the scatterplot results can be seen in the following figure.
Figure 4.3 Heteroskedasticity Test Results in Graph Scatterplot
Source: SPSS output the results of the primary data that have been processed, 2016
From the scatterplot graph in the image above can be seen that the dots randomly spread, and spread on top and below zero on
the Y axis It can be concluded that there is no heteroskedasticity in regression models Ghozali, 2011:107.
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E. Hypothesis Test Results
