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c. Autocorrelation Test
Autocorrelation test aims to test something, in a linear regression model. There is a correlation between the error of a bug in the period t
to bug errors t-1 period or previous period Ghozali 2013:110. Diagnose the autocorrelation done through testing to test the value of
Durbin Watson DW test by Ghozali 2013:111. Basis for decision- making as follows:
1 If 0 DW DL there is any positive autocorrelation. 2 If DL Dw Du or 4-Du D 4-DL uncertain conclusion.
3 If 4-DL Dw 4 there is any negative autocorrelation. 4 If 0 Dw DL or Du Dw 4-Du there is no autocorrelation.
d. Heteroscedasticity Test
According to Ghozali 2013 : 139, the aim from heteroscedasticity test is to test whether the regression model occur the variance
inequality of the residual from one observation to another observation. If the variance from residual of one observation to other observations
is fixed, it is called homocedasticity and if it different called heteroscedasticity. The presence of heteroscedasticity can be seen from
the graph Scatterplot between the predicted value of the dependent variable is ZPRED with residual SRESID. If there is a pattern like dots
are there forms a particular pattern of regular, then there
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heteroscedasticity. Conversely, if there is no clear pattern as well as points that spread then there is no heteroscedasticity.
3. Hypotesis Testing
a. Test Coefficient of Determination R
2
The coefficient of determination R
2
was essentially measure how far the models ability to explain variation in the dependent variable.
Determination coefficient is between zero and one. Small value of R
2
is the ability of independent variables in explaining the dependent variable is very limited. Value close to one means that the independent
variable gives almost all the information needed to predict the variation in the dependent variable Ghozali, 2013: 97.
Coefficient determination is a statistical measurement of how well the regression line approximates the real data point. By knowing the
value of R
2
, It can determine the magnitude contribution of independent variables toward the dependent variable. R
2
expresses a value between zero and one. If R
2
is near to 0, most of data variations cannot be explained by the regression model. In this case, the
regression model fits the data poorly. On the other hand, if R
2
is near to 1, most of the variation in the dependent variable can be explained
by the regression model. In other words, the regression model fits the data well Sekaran, 2010.
