Detecting and Treatment the Effect of Outlier Values in Linear Decision Making Models with Unrestricted Variables and Linear Regression Models (case study)

Abstract

          The appearance of outlier values in the set of data effect the result of the statistical analysis of data. Then the correct of decision making there   for study and estimate the outlier values the detection methods. This problem was studied in some of linear programming and linear regression but did not studied in linear programming with unrestricted variables which is important subject in many fields of operations research and its ability in treatment with unrestricted variables decision. In this paper we detect and estimate the outlier values in those models and hoe these    values effect the solution of the outlier values . The Hat matrix was used to determine and process the outlier constraint so that the new model will     be more suitable to match the requirements. The result show the    simplicity and accurate calculations also the clearly in determine  the outlier values