Scope of Research Sampling Method

51 b. Multicollinearity The independent variables which contain of multicollinearity make the coefficient of regression become unsuitable with the substances, thus the interpretation become inappropriate Fadhliyah, 2008 Multicollinearity can also be detected by making the correlation matrix between the independent variables and the significance of these correlations. A strong multicollinearity is worth 0.8 according to Ghozali 2013:82. c. Autocorrelation According to Ghozali 2013:138 autocorrelation test aims to test whether in a linear regression model is no correlation between confounding errors residuals in period t with the error in period t-1 previously. if there is a correlation, then there is a problem called autocorrelation. autocorrelation arises because observations over time are related to each other. This problem arises because the residuals are not free from the obsevation to another observation. How to detect the presence of autocorrelation: 1. Test Durbin-Watson DW-Test Test Durbin - Watson is only used for autocorrelation level one and requires the intercept in the regression model and no variable lag between independent variables. The hypothesis to be tested are: 52 a. H : no autocorrelation ρ = 0 b. H 1 : no autocorrelation ρ ≠ 0 Decision making whether there is autocorrelation: Null Hypothesis Decision If No positive autocorrelation Reject 0dd L No positive autocorrelation No Decesion d L ≤d≤d U No negative autocorrelation Reject 4-d L d4 No negative autocorrelation No Decesion 4-d U ≤d≤4-d L No positive and negative autocorrelation Not Rejected d U d4-d U Note: d U : Watson upper durbin d L : Durbin Watson lower d. Heterocedasticity Regression models should qualify BLUE order accuracy in depicting the actual circumstances, namely 1 the best 2 linear 3 unbiased 4 estimator. To determine eligibility BLUE regression model can be used heteroscedasticity test. Meanwhile, according to Gujarati 2003:321, the regression is still being done on the data containing heteroscedasticity will produce misleading conclusions. According Ghozali 2013:93 there are several statistical tests that can be used to detect heteroscedasticity, 1 Glejser, 2 White, 3 Breusch-Pangan-Godfrey, 4 Harvey 5 Park. In White test, the probability chi-square from ObsR-square 0.05 so heteroscedasticity is rejected.