Significance of the Effect of Quality on Decision: Rejection of Ho and Acceptance of Hi a. Dependent Variable: SB2 • The model shows R = 0.211. This means that the relationship (Relation) between quality and decision is 21.1%. This means that the relationship is not close. The greater R means the closer 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 0.044 means that 0.44% of the decision variable can be explained by quality, while the remaining 99.56% 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 0.75950. then the smaller the standard deviation means the better the model. • The table shows that the significant value is 0.000 (smaller than 0.05), meaning that quality has a positive and significant effect on decisions. This means reject Ho and accept Hi. • The regression equation is Y = 4.013 + 4.29 X, meaning that if the quality is increased by one unit, it will increase the decision by 4.29. b. Multiple Regression Determination Test • R = 0.211 means that the relationship (relation) between BI1, BI2, BI3, BI4, CS1, CS2, CS3, CS4 and SB2 (Y) is 21.1%, meaning that the relationship is not tight. The greater R means the closer the relationship. Value 0.0 - 0.19 0.2 - 0.39 0.4 - 0.59 Interpretation Very heavy Not heavy Quite heavy 0.6 - 0.79 0.8 - 0.99 Erat Sangaterat • R Square of 0.044 means that 0.44% of Loyalty can be explained by BI1, BI2, BI3, BI4, CS1, CS2, CS3, CS4 while the remaining 99.56% can be explained by other factors not examined by this study. • Adjusted R Square of 0.003 means that 0.03% of Loyalty can be explained by BI1, BI2, BI3, BI4, CS1, CS2, CS3, CS4 while the remaining 99.97% can be explained by other factors not examined by this study. • Standard Error of Estimated means measuring the variation of the predicted value. Standard Error of Estimated can also be called standard deviation. In the table, the Standard Error of Estimated is 0.75950. the smaller the standard deviation means the better the model and vice versa if the standard deviation is greater, the worse the model. ANOVA (F TEST) • The table above reveals that the calculated F value is 4.71 with a significant level of 0.000. while the F table at the 95% confidence level (0.05). therefore thecalculation of F count> F table and a significant level of 0.000 <0.05, indicating that the effect of independent variables BI1, BI2, BI3, BI4, CS1, CS2, CS3, CS4 as a whole is positive and significant on loyalty (Y). • The table shows that regression df = 8 means that the number of independent variables is 8, namely BI1, BI2, BI3, BI4, CS1, CS2, CS3, CS4. • Total df = 89 means the number of respondents/data is 89 (where df = n-1) while the residual df = 81 comes from 89-8. • Mean Square: Sum of Square/df. In the mean square regression table (2.72) = 2.176/8.