50 Guidelines for decision making are as follows:
a Value sig. or significance or probability value 0.05 distribution is not normal.
b The value of sign. Or significance or probability value 0.05 distribution is normal.
3.5.2 Multicollinearity Test
According to Ghozali 2005, this test is used to determine whether there is a correlation between independent variables in the regression model. A good
regression model should not have correlation between independent variables. If there is a correlation between independent variables, these variables are not
orthogonal. Orthogonal variable is the independent variable that value of a correlation between fellow independent variables is zero. To detect there is or no
multicollinearity in regression models can be seen from the tolerance value or the variance inflation factor VIF. See as the basis it can be concluded:
1. If the tolerance value 0.1 VIF value 10, it can be concluded that there is no multicollinearity between independent variables in the
regression model. 2. If the tolerance value 0,1 VIF value 10, it can be concluded that
there is multicollinearity among the independent variables in the regression model.
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3.5.3 Autocorrelation Test
Autocorrelation test aimed to test whether linear regression model has correlation between fault disturber in the t period with fault disturber in the period
t-1 earlier. If there is a correlation, so there is a problem of autocorrelation. Autocorrelation arises due to successive observation at all times in relation to
another. This problem occurs because the residual fault disturber is not free from one observation to another observation, usually found in time series data. The
consequences of the presence of autocorrelation in regression model is a variance sample cant describe the population variance, so the result of regression model
can’t be used to estimate the value of dependent variable in value of certain independent Ghozali, 2005
To detect autocorrelation, statistical tests can be done through test Durbin- Watson DW test Algifari, 2000. The basic decision can be there is or no
autocorrelation is:
Table 3.2 Autocorrelation
INTERVAL DECISION
1 1,1 – 1,54
1,55 – 2,46 2,46 – 2,9
2,9 There is autocorrelation
Without conclusion There is no autocorrelation
Without conclusion There is autocorrelation
Source : Algifari 2000
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3.5.4 Heterocedasticity Test
