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Another way to normality test can be done by looking at the spread of the data dots on the diagonal axis of the graph or by looking at the
histogram from the residual. Basic decision-making, namely Ghozali, 2013: 163:
1 If the point spread around the diagonal line and follow the direction of the diagonal line, the regression model to meet the
assumptions of normality. 2 If the point spread away from the line or diagonal and do not
follow the direction of the diagonal line, the regression model did not meet the assumptions of normality.
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 happen 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 VIF. Multicollinearity
views of the tolerance value 0.10 or VIF 10. Both of these measurements indicate each independent variable which is explained
by the other independent variables.
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c. Autocorrelation Test
Autocorrelation test aims to test something, in a linear regression model. There is a correlation between the error of a bug in the period t
to bug errors t-1 period or previous period Ghozali 2013:110. Diagnose the autocorrelation done through testing to test the value of
Durbin Watson DW test by Ghozali 2013:111. Basis for decision- making as follows:
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 4-DL Dw 4 there is any negative autocorrelation. 4 If 0 Dw DL or Du Dw 4-Du there is no autocorrelation.
d. Heteroscedasticity Test
According to Ghozali 2013 : 139, the aim from heteroscedasticity test is to test whether the regression model occur the variance
inequality of the residual from one observation to another observation. If the variance from residual of one observation to other observations
is fixed, it is called homocedasticity and if it different called heteroscedasticity. The presence of heteroscedasticity can be seen from
the graph Scatterplot between the predicted value of the dependent variable is ZPRED with residual SRESID. If there is a pattern like dots
are there forms a particular pattern of regular, then there
