Normality Test Multicollinearity Test Heteroskedasticity Test

1.0 0.8 0.6 0.4 0.2 0.0 Observed Cum Prob 1.0 0.8 0.6 0.4 0.2 0.0 E xpect ed C um P rob Dependent Variable: Y Normal P-P Plot of Regression Standardized Residual 1.0 0.8 0.6 0.4 0.2 0.0 Observed Cum Prob 1.0 0.8 0.6 0.4 0.2 0.0 E xpect ed C um P rob Dependent Variable: Y Normal P-P Plot of Regression Standardized Residual - Indicator 8 - Indicator 9 - Indicator 10 - Indicator 11 0,920 0,920 0,760 0,862 0,198 0,198 0,198 0,198 Valid Valid Valid Valid Source: primary data, 2010 From tables 4.4 obtained that all indicators used to measure the applied variables in this research has higher correlation coefficient than r table = 0,198 r table value of n=100. So, all indicators are valid.

4.5. Classic Assumption Test

This research applies two multiple linear regression models. A good regression model must free of classic assumption problems. Following is classic assumption test for both regression models.

4.5.1. Normality Test

Normality test was done by using testing to residual value. While the test was done by using P-P Plot. Normality test result can be visibly seen from the following figure. Figure 4.5 Normality Test Result Model 1 Model 2 .564 1.773 .579 1.726 .582 1.718 .447 2.239 .578 1.730 .591 1.692 .469 2.132 .390 2.562 .328 3.045 X1 X2 X3 X4 X5 X1X5 X2X5 X3X5 X3X5 Model 1 Tolerance VIF Collinearity Statistic s .615 1.626 .623 1.606 .613 1.631 .517 1.933 X1 X2 X3 X4 Model 1 Tolerance VIF Collinearity Statistic s Source: primary data, 2010 The figure indicates that residual points from both regression model have normal distribution because the points disseminating around diagonal line. Thereby normality condition required as statistical testing by using regression can be fulfilled.

4.5.2. Multicollinearity Test

A variable which shows multicollinearity symptoms can be seen from its high VIF Variance Inflation Factor value in a regression model. VIF value higher than 10 showing the existence of multicollinearity symptom in modeling regression. Result of VIF test from both regression models are as follows: Table 4.22 Multicollinearity Test Model 1 Model 2 Source: primary data, 2010 Result of the test indicates that all variables applied as regression model predictor shows sufficiently small VIF values, where altogether below number 10. It means that free variables applied in research doesnt show existence of multicollinearity symptom, then each independent variables serve as independent predictor.

4.5.3. Heteroskedasticity Test

Heteroskedasticity test was done using scatter plot. If there is no regular pattern at its residual points, hence no heteroskedasticity problem detected. Result of the test shown in the following figure. Figure 4.6 Heteroskedasticity Test Result Model 1 3 2 1 -1 -2 -3 Regression Standardized Predicted Value 4 3 2 1 -1 -2 -3 R egressi on S tudent iz ed R esi dual Dependent Variable: Y Scatterplot Model 2 Source: primary data, 2010 Heteroskedasticity test result shows there is no independent variable which significantly relates to absolute residual value. It means that both regression models dont have the symptom of heteroskedasticity existence. 4.6. Regression Analysis 4.6.1. Linear Regression Analysis Model 1 Testing of Hypothesis 1 - 4