<![CDATA[Multicollinearity Test This test aims to determine whether there is a correlation for the independent variables in the regression model used. A good regression model is a regression model that is non-multicollinearity, meaning that between one independent variable and another in the regression model are not perfectly interconnected. The size of the correlation that is free of multicollinearity is 5 or can be measured through the tolerance value or VIF (Variance Inflation Factor) of each variable in the SPSS ver. 21.0 computer program, namely if the tolerance value is 1.0 or VIF 5, it indicates the presence of multicollinearity. 21.0, namely if the tolerance value is 1.0 or VIF 5, it indicates the presence of multicollinearity. SPSS output ver. 20.0 to explain whether there are symptoms of multicollinearity between independent variables. Autocorrelation Test The autocorrelation test aims to test whether in a linear regression model there is a correlation between confounding errors in period t and confounding errors in period t-1 (previous). The following are the results of the autocorrelation test using SPSS 20.0:]]>
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