Regression Equation in Explaining the Effect of BI and CS Variables on Customer Loyalty TEST • • • • • • • • TheBI 1 variable has a positive and significant effect on loyalty. TheBI 2 variable has a positive and significant effect on loyalty. TheBI 3 variable has a positive and significant effect on loyalty. TheBI 4 variable has a positive and significant effect on loyalty. TheCS 1 variable has a positive and significant effect on loyalty. TheCS 2 variable has a positive and significant effect on loyalty. TheCS 3 variable has a positive and significant effect on loyalty. TheCS 4 variable has a positive and significant effect on loyalty. Model Summary Adjusted R ModelR R Square Square Std. Error of the Estimate 1 .245a .060 -.033 .69657 a. Predictors: (Constant), CS4, BI2, CS3, BI1, CS2, BI3, BI4, CS1 ANOVAa Sum of df ModelSquares 1 Mean Square Regression 2.520 8 .315 Residuals 39.302 81 .485 Total 41.822 89 F .649 Sig. .734b a. Dependent Variable: SB4 b. Predictors: (Constant), CS4, BI2, CS3, BI1, CS2, BI3, BI4, CS1 Coefficientsa ModeltSig 1 (Constant) 3.964 .911 BI1 -.061 .109 BI2 -.125 BI3 . 4.353 .000 -.071 -.556 .579 .109 -.152 -1.152 .253 .179 .112 .216 1.600 .114 BI4 -.019 .110 -.024 -.176 .861 CS1 .146 .129 .154 1.132 .261 CS2 -.001 .099 -.002 -.014 .989 CS3 -.147 .120 -.159 -1.231 .222 CS4 .035 .086 .049 .406 .686 a. Dependent Variable: SB4 1) a). The model shows R = 0.245, meaning that the relationship between quality and decision is 24.5%. This means that the relationship is not close. The greater the R means the tighter the relationship. To ensure the type of relationship between variables can be seen in the following table: Value 0.0 - 0.19 0.2 - 0.39 0.4 - 0.59 0.6 - 0.79 0.8 - 0.99 Interpretation Very heavy Not heavy Quite heavy Erat Sangaterat • R square of 0.060 means that 0.60% of the decision variable can be explained by quality, while the remaining 99.40% can be explained by other factors examined by this study. • Standard Error of Estimate means measuring the variation of the predicted value. Standard Error of Estimate can also be referred to as standard deviation. The Standard Error of Estimate is 0.69657. then the smaller the standard deviation means the better the model. b). In the table, it can be seen that the significant value is 0.000 (more than 0.05), meaning that quality has a positive and significant effect on decisions. Means reject Ho and accept Hi. The regression equation is Y = 3.964 + 7.13 X, meaning that if we increase the quality by an amount of One-unit will increase the decision by 7.13.