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b. Autocorrelation Test
Autocorrelation test aims to test if there is correlation in linear regression model between disturbances in t period with period t-1 previous period Santoso,
2010: 213. If correlation occurs, it refers to autocorrelation problem. It occurs because sequential observation along with time series. This problem appears because
residual disturbance is not free from one observation to another observation Ghozali, 2006: 99. It is often found in time series because of disturbance in
individual or group tends to influence disturbance in the same individual or group in the next period Ghozali, 2006: 100.
In cross-section data, autocorrelation problem relatively rarely occurs because disturbance in different observations come from different individual or group
Ghozali, 2006: 99. A good regression model is one which free from autocorrelation Santoso, 2010: 213.
This research uses the Durbin-Watson test suggested by Santoso 2010. To detect autocorrelation, there are some accepted frameworks, such as:
D-W value is lower than -2 indicate there is positive autocorrelation. D-W value is in between -2 and +2 indicate no autocorrelation.
D-W value is more than +2 indicate there is negative autocorrelation.
c. Heteroscedasticity Test
Heteroscedasticity test aims to test if there is variance difference from residual of one observation to another observations occurs Santoso, 2010: 207.
Furthermore, if the variance remains constant, it is called homoscedasticity and if it is changing or different, it is called heteroscedasticity Santoso, 2010: 207. Most cross-
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section data include heteroscedasticity because it collects data which represent any kind of measurements Ghozali, 2006: 125.
To test homoscedasticity test, the more practical method is by describing relationship between residual scores of regression model –difference of prediction
score and real one Santoso, 2010: 208. In addition, Ghozali 2006 stipulates to detect heteroscedasticity, but this research focuses only on graph analysis.
Graph analysis can be conducted by viewing plot graph between dependent variable’s prediction score –ZPRED- with its residual, SRESID. Detection of
heteroscedasticity can be conducted by analyzing distribution pattern in scatterplot graph between SRESID and ZPRED, where Y axis is a predicted Y and X axis is
residual Y prediction – Y actual which has been studentized Ghozali, 2006: 125. Decision making rationale Santoso, 2010: 210:
If there is a specific pattern, like dots which form well-ordered pattern waving, spreading then narrowing, it indicates heteroscedasticity occurs.
If there is no well-ordered pattern, and the dots spread above and below 0 in Y axis, so heteroscedasticity does not prevail.
d. Normality Test
Normality test aims to test if in a regression model, residual score of regression has a normal distribution Ghozali, 2006: 147. If distribution of residual
scores is not normally distributed, then it indicates a problem in normality assumption Santoso, 2010: 210. As commonly known that t and F test assume residual score
follows normal distribution. If this assumption ignored, then statistical test is not valid