65 Total mean of influence of consumer decision is 3.74. Following to the
likert scale above, it shows that correspondents of this research are attending to “neutral”. The indicators show the almost of respondents are agree. Its explained
Products offered through the internet has a competitive price is 3.77, Cheaper product prices when shopping over the Internet from the ordinary the store is
3.65, Products on the Internet often give a discount is 3.79, Products on the Internet has variable values is 3.66, The products on the internet have a significant
price is 3.64, I can compare prices on each different type of goods in each store online is 3.82, By shopping through the Internet, I often get a product with a
lower price is 3.86.
C. Classical Assumption Test
1. Autocorrelation
Table 4.11 Durbin Watson
Durbin Watson Conclusion
Less than 1,10 Autocorrelation available
1,10 and 1,54 Without conclusion
1,55 and 2,46 No autocorrelation available
1,46 and 2,90 Without conclusion
More than 2,91 Autocorrelation available
Source: Muhammad firdaus 2004:101
66 Autocorrelation test in certain model is aimed to know the availability of
correlation between disturbance variable e1 in the previous period et-1. Autocorrelation test can be made by using Durbin Watson Test.
Table 4.12
Autocorrelation Test Model Summary
b
a.
Predictors: Constant: PP, SE, CS b. Dependent Variable: The influence of consumer decision on online shopping
Source: Processed data by SPSS
To know whether there is autocorrelation or not, it can be seen through a certainty in the table 4.12 Durbin Watson. The result of Durbin Watson test used
SPSS is 2.114 with the standard of significant 0.05, according to the table 4.12, there is no autocorrelation in this linear regression models, and this model is
suitable to be used. Model
Durbin-watson 1
2.114
67
2. Multicollinearity Test
Table 4.13
Multikolinearity Test
a.Dependent Variable: The influence of consumer decision on online shopping
Based on the table 4.13 that the value of tolerance is not less than 0.1 and the value of Variant Inflation Factor not more than 10, meaning that this analysis
does not indicate there is a tendency towards variable multikolinearitas research, so that it can be concluded that this research is supported by classical theory and
reasonable use in testing. Collinearity Statistics
Model Tolerance
VIF Constant
Product Perception .852
1.174 Shopping Experience
.847 1.180
1
Customer Service .963
1.038
68
3. Heteroskesdastisity
Graphic 4.1
__
To find is there any or no Heteroskesdastisity is using scatterplot that referred to graphic of plot between prediction value dependent ZPRED and the
residual ZRESID, when Y has predicted and X is residual. In the output SPSS 16 for windows scatter plot shows the dots that described is spread above and below
or around 0 number and did not formed specific pattern. Hence could be concluding as the regression model did not have any serious problem. In the other
words that residual variance model from one observation to other observation is constant or homokedastisitas. This finding shows that multiple regression model
is suitable to be used in this research.
69
4. Normality Data
