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2. Multicolinearity Test
According Priyatno 2011: 288 multicolinearity test is used to test whether the regression model found a correlation between the independent
variables. A good regression model should not happen correlation between independent variables. Testing method used is to look at the value Variance
Inflation Factor VIF and Tolerance in the regression model. If the VIF value tolerance of less than 10 and more than 0.1 then the regression model free of
multicolinearity.
Table 4.9 Test multicolinearity
Coefficients
a
Model Collinearity Statistics
Tolerance VIF
1 Constant
Liquidity .637
1,571 Profitability
.941 1,062
Solvency .627
1595 Company Size
.701 1,427
a. Dependent Variable: Capital Structure Source: Data processed Author, 2015
Results of the analysis of the table above, note that the value of the variable Liquidity has VIF = 1,571 with the value of Tolerance of 0637.
Profitability has the value of VIF = 1,062 with the value of Tolerance of 0941. Solvency has the value of VIF = 1.595 with a tolerance value of 0627.
75 Company size has the value of VIF = 1,427 with the value of Tolerance of
0701. Thus, based on data and analysis results in Table multikolinearitas test
conditions, it is known that the three independent variables have VIF value does not exceed 10, and the value of Tolerance under the smaller than the
number 1. This means, there are three independent variable symptoms problems
multicolinearity.
3. Auto Correlation Test
According Priyatno 2011: 292 autocorrelation test aims to test whether the linear regression model was no correlation between bullies error in period t
with bullies error period t-1 previously. Autocorrelation arise due to successive observations over time are related to each other. This problem arises because the
residual error bullies are not independent from one observation to another observation. It is often found in the time series data time series Due to residual
on a variable residual tends to affect the same variable in the next period. A good regression model is a regression that is free of autocorrelation. To detect the
presence or absence of autocorrelation then tested the Durbin-Watson DW, to see how many samples and independent variables studied were later seen on a
number of its provisions Durbin-Watson tables.
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Table 4.10 Auto Correlation Test
Model Summary
b
Model R
R Square Adjusted R
Square Std. Error of
the Estimate Durbin-
Watson 1
.852a .725
.705 5.52724
2,134 a. Predictors: Constant, Company Size, Profitability, Liquidity, Solvency
b. Dependent Variable: Capital Structure Source: Data processed Author, 2015
Figure 4.2 Model Autocorrelation
Source: Data processed Author, 2015. From processing SPSS 18.0 obtained value DW amounted to 2,134, this
value will be compared with the value of the table with significant value of 5, the number of samples 60 n and the number of independent variables 4 k = 4,
because the value DW 2,134 is located in the area dU dW 4-dU 1,727 2,134 2,273, it can be concluded that there is no autocorrelation the regression model.
dL 1,444
dU 1,727
DW 2,134
4-dU 2,273
4-dL 2,557
4
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4. Heteroskidastity Test
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