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3. Classic Assumption Test
a. Normality Test
Testing normality is a test of the normality of data distribution. Test is a test of the most widely performed by parametric
statistical analysis. The use of normality tests becausethere is a parametric statistical analysis, the assumptionsthat must be owned
by data is that the data are normally distributed Ghozali, 2005:110.
The purpose normally distributed data is that data will follow a normal distribution form. There are two ways to detect whether or
not residual normal distribution, those are with graph analysis and statistical tests. One of the easiest ways to see the normality of
residuals is to look at a histogram graph comparing observational data with the distribution of near-normal distribution. Normal
distribution will create a straight line diagonal and plotting residual data will be compared with the diagonal line. If the
residual data distribution is normal, then the line that describes the actual data will follow the diagonal line. Normalitytest used
whether the regression model contained in a normal distribution or not between dependent and independent variables. In this
research, the normality test using normal p-p plot histogram chart Ghozali, 2005:110.
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b. Multicollinearity Test
According to Ghozali 2005:91, multicollinearity is used to indicate the existence of linear relationships between free
variable Independent in the regression model. If the independent variable, it can be perfectly, correlated with
perfect multicollinearity. According to the Wibowo, 2012: 87 one way to detect
multicolinearity is to use a test tool called Variance inflation factor VIF. If the value of VIF 10 it shows on the model
there are no symptoms of multicollinearity. In addition to the other methods that can be done is to correlate between the
independent variables. When the value of the correlation between the independent variable is not greater than 0.5 then
the model there is no multicollinearity.
c. Heteroscedasticity Test
This test assumption aims to find out whether in a regressionmodel, there was inequality a variance of residual of
one observation and other observation.The model is said to have a problem if there is a variance heteroscedasticity
variables in the model are not the same. Thesesymptoms can also be interpreted that the variance of the
model residual inequality occured in the regression model observations Ghozali, 2005: 105.
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According to Wibowo, 2012: 93 if the result has a significance probability value alpha values 0.05 then the
model is not experiencing heteroscedasticity.
d. Autocorrelation Test
Autocorrelation test is used for the sole purpose of knowing whether there is a correlation between members of a set of
observed data and analyzed according to a cross section. This test aims to see whether there is residual in an observation with
other observation on the model. In these research, researcher using Durbin Watson test. A model can be expressed is not the
case if probability value autocorrelation symptoms Durbin Watson 0.05 Wibowo 2012: 106.
4. Multiple Regression Analysis
Multiple Regression involves a single dependent variable and twoor more independent variables. The questions raised in the
context of bivariate regression can also be answered via multiple regression by considering additional independent variables.
Malhotra, 2004: 512. The general form of the multiple regression model is as follows:
Y = βo + β1X1 + β2 + X2 + β3 + X3 + ... + βkXK + e Which is estimated by following equation:
Y = a + b1x1 + b2x2 + b3x3 + ...... bkxk That is means about the research model:
Y: a + b1x1 + b2x2 + b3x3+ e
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