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Linear Programming in Exploratory Data Analysis

Associate Professor, Department of General Business, BEB 600, The University of Texas, Austin, Texas 78712

Edward L. Frome

Research Scientist, Oak Ridge Associated Universities, Oak Ridge, Tennessee 37830

Michael G. Sklar

Assistant Professor, College of Business, University of Georgia, Athens, Georgia 30602

It has long been popular to utilize the least Squares estimation procedure for fitting the multiple linear regression model to observed data. In this paper, two useful alternatives to least Squares (L₂ norm) estimation in exploratory data analysis are examined: least absolute value estimation (L₁ norm) and Chebychev (L norm) estimation. Formulating the L₁ norm and L norm problems as linear programming problems offers several advantages, including efficient Solution methods using special-purpose Computer codes. An example is provided in which the three procedures are used to fit a line, both with and without an outlier present in the data.

Key Words: Linear programming • Data analysis • Least squares • Least absolute value • Chebychev estimation

Journal of Educational and Behavioral Statistics, Vol. 5, No. 4, 293-307 (1980)
DOI: 10.3102/10769986005004293


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