13
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q u
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c y
Ex p
e c
te d
C u
m P
ro b
Table 4.3 Descriptive Statistics of All Variables during 2010--2012
Descriptive Statistics
N Minimum
Maximum Mean
Std. Deviation PROF
DER DVP
MVC Valid N listwise
90 90
90 90
90 -0.19
-31.78 0.00
-93.49 0.42
40.37 365.00
39.47 0.1029
0.9630 23.4778
2.9281 0.11068
5.53641 47.31397
11.65168
Source: Secondary Data processed, 2014 C. Classical Assumption Test
1. Normality Test Figure 4.1
Histogram in Manufacturing Company
Histogram Dependent Variable: MVC
25 20
15 10
5 -3 -2 -1 0
1 2 3 4
Mean =
Regression Standardized Residual
Source: Secondary data were processed, 2014
-3.47E-18 Std. Dev. =...
Figure 4.2 Normal Probability Plot Manufacturing Company
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: MVC
1.0 0.8
0.6 0.4
0.2 0.0
0.0 0.2
0.4 0.6
0.8 1.0
Observed Cum Prob
Source: Secondary data were processed, 2014
14
Table 4.4 Normalities ManufacturingCompany
One-Sample Kolmogorov-Smirnov Test Unstandardized
Residual
N Normal Mean
Parametersa,b Std. Deviation Most Extreme Absolute
Differences Positive Negative
Kolmogorov-Smirnov Z Asymp. Sig. 2-tailed
90 0.0000000
2.32195453 0.071
0.053 -0.071
0.670 0.760
a Test distribution is Normal. b Calculated from data.
Source: Secondary Data processed, 2014 Based on the results in Table 4.4 above, the data is normally distributed.
This is indicated by the value of the Kolmogorov - Smirnov for 0.670 and 0.760 is significant at greater than 0.05. This means that the data is normally distributed
residual, because the significance value is more than 0.05.
2. Multicollinearity Test
Multicollinearity in the regression can be seen from the value of Tolerance and Variance Inflation Factor VIF.
Table 4.5 The Multicolinearity Test Result of Manufacturing Company
Model Collinearity
Statistics Tolerance
VIF 1
Constant PROF
0.895 1.118
DER 0.987
1.013
15
DVP 0.906
1.104 a. Dependent Variable: MVC
Source: Secondary data were processed, 2014 A regression model is free of multicollinearity problem if it has a value
under 1 and VIF tolerance under 10. This show is not the case in the model multicollinearity.
3. Autocorrelation test Table 4.6
The Autocorrelation Test Result of Manufacturing Company
Model Durbin-Watson
1 1.984
a Predictors: Constant, DVP, DER, PROF b Dependent Variable: MVC
Source: Secondary Data processed, 2014 Based on the results of the regression analysis on the data value
Manufacturing Company Durbin Watson DW of 1.984, DW-table size: dL outer boundary = 1.589; dU within limits = 1.726; 4-dU = 2.274, and 4-dL =
2.411. Because dU d 4-dU, 1.726 1.984 2.274 these results indicate that the regression model is there is no autocorrelation.
R e
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s s
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4. Heteroscedasticity
