Industrial robot manipulators require high absolute position accuracy of the end effector to perform precise and complex tasks. However, manufacturing errors cause differences between nominal and actual parameters, and errors between the expected and actual positions of the end effector, resulting in undesired lower absolute position accuracy. Accordingly, to increase the absolute position accuracy of the end effector, kinematic calibration is required to correct the nominal parameters close to the actual parameters. However, in this study, redundancy of parameters may occur from the overlapping degrees of freedom of parameters in adjacent frames, which causes the problem of unnecessarily correcting many parameters in the optimization process. Thus, to solve this problem and use only the necessary parameters, this paper focuses on the linear relationship of redundant parameters and proposes a method of automatically discriminating and removing it through the Pearson Correlation Analysis. Additionally, through simulations on the two manipulator models, we verify the accuracy of redundancy of parameters determined by the proposed method, and demonstrate consistency and efficiency by comparing the results before and after redundancy removal.