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Y-26 0,764
Valid 0,747
Reliable
Y-27 0,468
Valid 0,810
Reliable
Y-28 0,570
Valid 0,812
Reliable
Y-29 0,650
Valid 0,773
Reliable
Y-30 0,722
Valid 0,758
Reliable
Source: Processed Data, 2015 The results showed that of the 30 items of the statement
given to 20 try out respondents have rated Corrected Item-Total Correlation is greater than the value of 0.30, which means all items
declared are valid Duwi Priyatno 2010: 90. Then from the try out of the data showed that all items CronbachsAlpha statement if
Item Deleted values greater than 0.60, which means all items declared as reliable Imam Ghozali 2005: 41
2. Descriptive Statistical Test
The research instrument can be assessed from the result of descriptive statistical test, the result can be seen in table 4.2:
Table 4.2 Descriptive Statistical Test
N
Min Max
MeanAverage Std.
Deviation
BWNS 60
22 28
24.67 1.75
BQ 60
20 28
24.1 2.12
BSO 60
25 32
28.47 2.08
BL 60
14 25
20.47 2.58
CPD 60
22 29
26.03 1.86
Source: Processed Data, 2015
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Table 4.2 shows the minimum value, maximum value, average value mean, and standard deviation value of each variable. The standard
deviation value of all variables is less than the average value, it means that the standard error in this instrument is low. The low standard error
indicates that the determination of variables in this research is good to be used and to be processed further.
3. Classical Assumption Test
a Normality Test Result Normality test to the data is to assess whether the data population
normally distributed. Good data and fit for use in research is one that has a normal distribution. Normality of data can be viewed in several ways,
including by looking at the normal curve p-plot. Figure 4.5
Normal Probability Plot
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Figure 4.5 shows that the variable is said to be normal because the distribution of the image data points are spread around the diagonal line,
and the spread of the data points in the direction to follow a diagonal line. b Multicollinearity Test Result
Multicollinearity test aims to assess whether in the regression model is found the existence of correlation between independent variables.
A regression model which there is no multicollinearity is when value scale correlation between independent variable is the value of VIF Variance
Inflation Factor less than 10 and has tolerance value more than 0.1. Table 4.3
Multicollinearity Test Result
Coefficients
a
Model Unstandardized Coefficients
Standardized Coefficients
t Sig.
Collinearity Statistics B
Std. Error Beta
Tolerance VIF
1 Constant
1.535 2.311
.664 .509
BWNS .258
.096 .224
2.687 .010
.691 1.447
BQ .422
.094 .431
4.495 .000
.520 1.924
BSO .155
.097 .156
1.601 .115
.504 1.986
BL .190
.075 .237
2.531 .014
.544 1.838
a. Dependent Variable: CPD
Source: Data processed, 2015 Table 4.3 shows that each variable has a tolerance score is above
0.1 and the Variance Inflation Score VIF score is around 1.4 until 1.9. In which Brand Awareness BWNS has tolerance level is 0.691 and the VIF
level is 1.447. The Brand Quality BQ has tolerance level is 0.520 and the
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VIF level is 1.924. The Brand Association BSO has tolerance level is 0.504 and the VIF Level is 1.986. The Brand Loyalty BL has tolerance
level is 0.544 and the VIF Level is 1.838. The table indicates that the regression equation is free from multicollinearity problems
c Heteroscedasticity Test Result Heterscedsticity test aims to test whether the regression model of
the residual variance inequality occur between an observation to another observation. The detection of heteroskedasticity can be seen from the
existence of certain pattern in Scatterplot Graph.
Figure 4.6 Scatterplot Graph
Source: Data Processed, 2015
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Figure 4.6 shows that the dots are spread randomly and do not form a certain pattern. This means that the heteroscedasticity problem does
not exist, and the regression model is feasible to be used for predicting customer purchase decision to the brand awareness, brand quality, brand
association and brand loyalty.
4. Hypothesis Test Result
