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depends on the type of scale of measurement. The descriptive statistical testing obtains a picture or describes data that can be seen from median,
mean, mode, standard deviation, variance, maximum and minimum.
2. Classic Assumptions Test
Classical test assumption aims to determine the relationship between the variables in the data. Before conducting regression analyzes,
first tested the classical assumptions to determine whether there is a relationship between the variables.
a. Normality Test
Normality test aims to test whether the regression model, or residual confounding variable has a normal distribution. There are two
ways to detect whether or not residual normal distribution, i.e. the graph analysis and statistical tests Ghozali, 2013: 160. Normality test
can use the tools such as statistical tests to Kolmogorov-Smirnov Z 1 - Sample KS, the basic decision-making Ghozali, 2013: 164:
1 If the value Asymp. Sig. 2-tailed less than 0.05, then H0 is rejected. This means that the data are not normally distributed
residuals. 2 If the value Asymp. Sig. 2-tailed of more than 0.05, then H0 is
accepted. This means that the data were normally distributed residuals.
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Another way to normality test can be done by looking at the spread of the data dots on the diagonal axis of the graph or by looking at the
histogram from the residual. Basic decision-making, namely Ghozali, 2013: 163:
1 If the point spread around the diagonal line and follow the direction of the diagonal line, the regression model to meet the
assumptions of normality. 2 If the point spread away from the line or diagonal and do not
follow the direction of the diagonal line, the regression model did not meet the assumptions of normality.
b. Multicollinearity Test
Multicollinearity test aims to test whether the regression model found a correlation between the independent variables Ghozali,
2013:105. A good regression model should not happen correlation between the independent variables. To detect the presence or absence
of multicollinearity in the regression model can be seen from the value of tolerance and the Variance Inflation Factor VIF. Multicollinearity
views of the tolerance value 0.10 or VIF 10. Both of these measurements indicate each independent variable which is explained
by the other independent variables.
