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2. Classical Test Assumption
a. Normality Test
According to Zulkifli Matondang 2009, normality tests are conducted in purpose to detect whether a set of data will be used as basic
start to test hypothesis is empirical data that meets the naturalistic nature. Naturalistic nature is a thought that phenomena symptoms occur in this
nature are natural and patterned. Widhiarso 2009 said that normality tests are some tests to measure whether our set of data having normality
distribution so it can be used in parametric statistic. Tests of normality become important because this is a parametric test and have to normal
distributed Haryadi and Winda, 2011. So, normality tests are some kind of tests to clarify whether the data obtained are normally distributed and,
importantly, represent the whole population or not. Researcher choose two tools to test whether the data is
distributed normally or not. 1 GraphAnalysis
According to Ghozali 2006 normality test can use histogram graph by seeing the form of curve in the graph Normal
Probability PlotP-P Plot namely with see at the spread of the
data dots on the diagonal axis from the normal chart. Basic for decision-making are:
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a For histogram graph, if the curve make a form of bell around the chart, so the regression model meet the normality
assumption b For Normal Probability PlotP-P Plot, if the data spread
around the diagonal line and follow the direction of the diagonal line, so the regression model meet the normality
assumption. 2 Statistical Analysis
Researcher uses tools of Lilliefors Kolmogorov- Smirnov because Haryadi and Winda 2011 suggested that if
data of testing are more than 50 i.e. respondents are more than 50 people then use Lilliefors Kolmogorov- Smirnov test. Criteria
for Lilliefors Kolmogorov- Smirnov test are: a Number of Kolmogorov- Smirnov significance Sig. 0.05,
indicates the data normally distributed. b Number of Kolmogorov- Smirnov significance Sig. 0.05,
indicates the
data are
not normally
distributed NovitaItalianiKatsuri, 2011.
b. Multicollinearity Test Multicollinearity test aims to test whether the regression model
found a correlation between the independent variablesGhozali, 2009:95. A good regression model should not correlate between the
independent variables. To detect the presence or absence of
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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. c. Autocorrelation Test
Autocorrelation is correlation between observed members arranged in time series if the data used is time series data or
correlation among four contiguous variables Andriyatno, 2010. Diagnose the autocorrelation done through testing to test the value of
Durbin Watson DW test by Ghozali2009:100.Here the criteria for testing autocorrelation.
1 If 0Dw DL there is any positive autocorrelation. 2 If DL Dw Du or 4-Du D 4-DL uncertain conclusion.
3 If 0 Dw DL or Du Dw 4-Du there is no autocorrelation. 4 If 4-DL Dw 4 there is any negative autocorrelation.
d. Heteroscedasticity Test According to Ghozali 2009, the aim of 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 andif it different called
heteroscedasticity. A good regression model is homocesdasticity or
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