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D. Data Analyze Method
The dependent variable in this study refers to the Local Government Expenditures, while the independent variables refer to the PAD, DAU and DAK.
The analytical tools used are the multiple regression multiple regression in order to see the influence on all these variables simultaneously. Tests carried out by
using the computer application program SPSS 21.0 for Windows.
1. Descriptive Statistics
Descriptive statistics give an overview or description of a data views of the value of the average mean, minimum, maximum and standard deviation
Ghozali, 2013:19. An overview of the data produces a clear information so that the data is easy to understand. In this research, by looking at an overview
of existing data, it will be obtained clear information regarding the influence of Original Local Government Revenues, General Allocation Fund and
Special Allocation Fund to the Local Government Expenditure .
2. Classical Assumptions
a. Normality Test Normality test is intended to determine whether the used data
is normally distributed or not. Normality test needs to be done to determine the statistical tools, so that the conclusions drawn can be
accounted for. There are two ways to detect whether or not residual normal distribution, i.e. the graph analysis and statistical tests
Ghozali, 2013: 160, namely:
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1 Graph Analysis One of the easiest ways to see the normality of the
residuals is to see a histogram graph comparing the observational data with the distribution which closes to normal
distribution. More reliable method is by looking the normal probability plots comparing to the cumulative distribution of a
normal distribution. Normal distribution will form a straight diagonal line and plot the data will be compared with the
residual diagonal lines. If the residual data distribution is normal, then the line that describes the real data would follow
the diagonal line Ghozali, 2013: 163. 2 Statistical Analysis
Simple statistical test can be done by looking at the value of the kurtosis and Z-values of skewness. Another statistical test
that can be used to test the normality of residuals is non- parametric statistical test of Kolmogorov-Smirnov KS, if the
significance level 0.05, then the data is normally distributed and can be performed multiple regression models Ghozali,
2013: 164. Guidelines for decision-making about the data close to or a normal distribution by Kolmogorov Smirnov can be seen
from: a Sig. or significantly or probability 0.05, then the data
distribution is not normal.
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b Sig. or significantly or probability 0.05, then the data distribution is normal.
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.
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.
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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, heteroscedasticity means
there is a variant that is not the same for different independent variables. It can be detected by observing how the points on the
scatterplot between the estimated values of Y with residual value the difference between the actual dependent variable predictive
values versus the value of the prediction spread or not to form a pattern. If the graph has a standardized residual axis of the X and Y
axis that has been predicted not to form a clear pattern wavy, widened, then narrowed, as well as scattered both above and below
the 0 on the Y axis Heteroscedasticity it did not happen.
Some ways to detect the presence or absence of heteroscedasticity:
1 If there is a specific pattern, such that there are points that form a regular pattern wavy, widened and then narrowed, it has
been indicated heteroscedasticity. 2 If there is no clear pattern, and the points spread above and
below the 0 on the Y axis, then it does not happen heteroscedasticity.
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3. The Result of Multiple Regression Test
Kategori