Comparing Least-Squares and Goal Programming Estimates of Linear Regression Parameter

Authors

  • Maizah Hura Ahmad
  • Robiah Adnan
  • Chik Kong Lau
  • Zalina Mohd Daud

DOI:

https://doi.org/10.11113/matematika.v21.n.519

Abstract

A regression model is a mathematical equation that describes the relationship between two or more variables. In regression analysis, the basic idea is to use past data to fit a prediction equation that relates a dependent variable to independent variable(s). This prediction equation is then used to estimate future values of the dependent variable. The least-squares method is the most frequently used procedure for estimating the regression model parameters. However, the method of least-squares is biased when outliers exist. This paper proposes goal programming as a method to estimate regression model parameters when outliers must be included in the analysis. Keywords: Method of least squares; outliers; goal programming.

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Published

2005-12-01

Issue

Section

Mathematics