Classic Assumption Test Analysis Result

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D. Analysis Result

1. Classic Assumption Test

a. Normality Test Normality test aims to test whether the regression model, the dependent variable customer satisfaction and independent variables variables customer service X1, store design and display X2, communication mix X3, location X4, merchandise assortment X5, and pricing X6 both have a normal distribution or not. If the distribution of the residual values can not be considered to be normally distributed, then it is said there are problems with the normality assumption. According Ghozali 2006: 149, the principle of normality can be detected by looking at the spread of the data dots on the diagonal axis of the graph probability plots or by looking at the histogram of the residual. Basis for decision making as follows: 1 Detection of the histogram, if the normal curve in the graph follow a bell shape, then the data are normally distributed. 2 While the detection of the normal probability plot on the graph, if the data dots spread around the diagonal line, and follow the direction of the diagonal line, then the regression model to meet the assumption of normality. If the spread of the data points do not 124 follow the direction of the diagonal, then the regression model did not meet the assumption of normality. 3 This statistical test that can be used to test the normality of the residuals is a statistical test of non - parametric Kolmogorov- Smirnov KS Ghozali, 2006: 151. Basis for decision making, when the value of the Kolmogorov Smirnov significance greater than 0.05, it can be said to be normally distributed data. If the value of the significance of the KS test is smaller than 0.05, it can be said the data was not normally distributed. Figure 4.1 Source : Processed primary Data by SPSS 1.7 125 Based on the figure above this research has done normality data disrtibution test. From the p-p plots above diagram above , it can bee seen that the plots are distributed along the diagonal lin. Thus it can be concluded that the data used in this research has a normal distribution. Figure 4.2 Source : Processed primary Data by SPSS 1.7 Based on the chart above the Histogram Graphic shows normal distribution . So that regression model requires normality assumes. 126 Table 4.50 One-Sample Kolmogorov-Smirnov Test Unstandardized Residual N 100 Normal Parameters a,,b Mean .0000000 Std. Deviation 1.45660083 Most Extreme Differences Absolute .049 Positive .049 Negative -.045 Kolmogorov-Smirnov Z .493 Asymp. Sig. 2-tailed .968 a. Test distribution is Normal. b. Calculated from data. Source : Processed primary Data by SPSS 1.7 Based on the figure above the value of the Kolmogorov Smirnov significance greater than 0.05, it can be said to be normally distributed data. 127 b. Multicollinearity test According to Ghozali 2006:95, multicollinearity test aims to test whether the regression model found a correlation among the independent variables customer service X1, store design and display X2, communication mix X3, location X4, merchandise assortment X5, and pricing X6. Good regression model should not happen correlation between the independent variables retail mix Consist of customer service, store design and display, communication mix, location, merchandise assortment, and pricing, If among the independent variables correlated with each other, then these variables are not orthogonal. To detect the presence or absence of multicollinearity among the independent variables in regression model, it can be seen from Tolerance and VIF value. Cutoff value commonly used to indicate wheter there is multicolliniearity or not is Tolerance value ≤0.10 or equal to VIF value ≥10. 128 Table 4.51 Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF 1 Constant 5.749 2.415 2.381 .019 Customer Service .142 .053 .277 2.691 .008 .717 1.396 Store Design and Display -.031 .067 -.045 -.459 .647 .798 1.254 Communication Mix -.070 .072 -.096 -.966 .336 .768 1.302 Location -.070 .060 -.114 -1.179 .242 .811 1.232 Merchandise Assortment .240 .130 .188 1.850 .068 .734 1.362 Pricing .483 .136 .351 3.560 .001 .783 1.277 a. Dependent Variable: Customer Satisfaction Source : Processed primary Data by SPSS 1.7 Based on the figure above the value of Tolerance is ≤0.10 and the value of VIF is ≥10. It can be said there is no multicollinearity among indepedent variables in regression model. c. Heteroscedasticity Test Heteroscedasticity test aims to test whether the regression model of the residual variance occurs inequality an observation to other observations. If the variance of the residuals one observations to other 129 observations stable, it is called different homoskedastisitas and if it is different called heteroscedasticity. Good regression models is that happened homoskedastisitas or did not happen heteroscedasticity Santoso: 2012: 238. 1 Looking at the scatterplot graph, if forming certain patterns, such as dots form a certain pattern regularly wavy, widened then narrowed, then heteroscedasticity indicates has occurred. If there is no clear pattern, and the points spread above and below the 0 on the Y axis, then there is no heteroscedasticity Santoso, 2012: 240. 2 Glejser Test Glejser test is done with the regressed absolute value of residuals against the independent variables customer service X1, store design and display X2, communication mix X3, location X4, merchandise assortment X5, and pricing X6. Guidelines from glejser test is looking at the significance level of each independent variables customer service X1, store design and display X2, communication mix X3, location X4, merchandise assortment X5, and pricing X6 on the dependent variable customer satisfaction. If the significance level yield number 0.05, it can be said regression model does not contain any heteroscedasticity. Ghozali, 2006: 129. 130 Figure 4.3 Source : Processed primary Data by SPSS 1.7 Based on the figure 4.5 there is no clear pattern and the points spread above and below the 0 on the Y axis, then it can be said there is no heteroscedasticity. 131 Table 4.52 Coefficients a Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 Constant 5.232 1.398 3.744 .000 Customer Service -.039 .030 -.148 -1.285 .202 Store Design and Display -.026 .039 -.075 -.684 .496 Communication Mix -.020 .042 -.054 -.484 .630 Location -.061 .035 -.191 -1.769 .080 Merchandise Assortment .130 .075 .196 1.729 .087 Pricing -.088 .078 -.123 -1.118 .267 a. Dependent Variable: RES_2 Source : Processed primary Data by SPSS 1.7 From the figure above the significance level is 0.05, it can be said regression model does not contain any heteroscedasticity. 132

2. Multiple Linear Regression Analysis