Application of Regression Tree in Salary Estimation of Team Players in Final of European Football Championship 2016

Regression tree is one of nonparametric data exploration methods that can be used to observe relationship between continuous response variables and explanatory variables that are large and complex. The explanatory variables considerably affect the response are variables that appear as a separator or partition. This study aimed to analyze the factors affecting the estimation of team player salary in final of European Football Championship 2016. The results illustrated that after trimmed by Minimum Complexity Cost rule, R^CV values obtained was 0.798 with classification accuracy level of 72.09%. Explanatory variables that partitioned were passing/header/shooting, became the dominant variable in explaining the salary estimation of team player in final of European Football Championship 2016.