<![CDATA[The results of the simple regression analysis between the price variable and the purchase decision (Y) show the t-count value obtained is 3.960. While t table obtained 1,666. Thus, because the value of tcount > from the value of ttable where 3.960>1.666, there is a significant effect of the price variable (X1) on the purchasing decision variable (Y). The value of the correlation coefficient (R) between the price variable (X1) and purchasing decisions (Y) is 0.380, which means the relationship is quite strong. The results of the regression analysis show that the value of 0.145 means that the purchase decision variable (Y) is influenced by the price variable (X1) by 15.5% and the remaining 85.5% is influenced by other X variables that are not included in this research model. The results of multiple linear regression analysis between the variables of price, product quality and service, on purchasing decisions show the Fcount value obtained is 26.616. While Ftable obtained 1,666. Thus, the value of Fcount> from the value of Ftable where 26.616>1.666 it can be concluded that there is a significant effect of the price variable (X1), product quality variable (X2), and service variable (X3) on purchasing decisions (Y). The R value obtained is 0.262, meaning that there is a strong relationship between the price variable (X1), product quality variable (X2) and service variable (X3) and the purchasing decision variable (Y). The results of the determinant analysis (R2) obtained a value of 0.529, meaning that the magnitude of the purchase\ng decision (Y) is influenced by the price variable (X1), the product quality variable (X2) and the service variable (X3) of 52.9% and the remaining 47.1% is influenced by another X variable that was not included in this study.]]>
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