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Table 4.24 Purchase decision of chips Maicih
Frequency Disagree
1 Neutral
14 Agree
58 Strongly Agree
27 Total
100
As shown in the table 4.24 above, 1 respondents stated disagree, 14 respondents stated netral, 58 respondents stated agree and 27 respondents
say strongly agree on the statement purchase decision of chips Maicih.
3. Classical Assumption Test
a. Multicollinearity Test Multicollinearity test aims to test a correlation among the
independent variable in the regression model. A good regression model should have no correlation among the independent variables. The result
acquired from SPSS 20 Statistic Software. The table below shows the multicollinearity test result.
Table 4.25 Multicolonearty Test
Coefficients
a
Model Collinearity Statistics
Tolerance VIF
1 Constant Product Differentiation
0.768 1.302
Image Differentiation 0.828
1.207 Word of Mouth
0.845 1.184
a. Dependent Variable: Purchase Decision
Source: Primary Data Output from SPSS 20
72
From the table above tolerance value more than 0.10 and VIF value less than 10 or VIF10, it can be concluded that there is no problem
multicollinearity among independent variables in regression model. 2. Heteroscedasticity Test
According to Duwi Priyatno 2012:158 glejser test is done by regressing between the independent variable with residual absolute value.
If the value of significance between independent variable with absolute residuals more than 0.05, so there is no problems heteroscedasticity
Heteroscesdasticity test is aimed to examine whether in the model occur any residual variance in certain monitoring period to other
monitoring period. If the characteristic is fulfilled, it means that the factors of intruder variation toward the data have the characteristic of
heteroscedasticity. The result acquired from SPSS 20 Statistic Software. The table below shows the Heteroscesdasticity test result.
Table 4.26 Heteroscedasticity Test
Coefficients
a
Model t
Sig. 1
Constant 0.083
0.934 Product
Differentiation -0.043
0.966 Image
Differentiation -0.075
0.940 Word of
Mouth 1.432
0.155 Source: Primary Data Output from SPSS 20
73
From the table above 4.26 above, it can seen If the value of significance between independent variables with absolute residuals more
than 0.05, so there is no problems heteroscedasticity 3. Normality Test
According to Duwi Priyatno 2012: 144 normality data test is aim to know the distribution of data in the variables that use in the research. A
good data used in the research is a data which has a normal distribution. Normality data can be seen from various ways, which is by looking at the
normal curve of P-P plot. A normal variable is when the diagram of distribution with the dots spreads around the diagonal line, and the
spreading of dots data is one same along diagonal line, it can be said that the data has a normal distribution. The result acquired from SPSS 20
Statistic Software. The table below shows the Heteroscesdasticity test result.
Figure 4.1 Normality Test
Source: Primary Data Output from SPSS 20
74
Based on figure 4.1 this research has done normality data distribution test. From the P-P Plots diagram above, it can be seen that
the plots are distributed along the diagonal line. Thus, it can be concluded that the data used in this research has a normal distribution.
4. Multiple Linier Regression
