Journal of Applied Mathematics and Decision Sciences
Volume 8 (2004), Issue 2, Pages 131-140

On the relationship between regression analysis and mathematical programming

Dong Qian Wang,1 Stefanka Chukova,1 and C. D. Lai2

1School of Mathematical and Computing Sciences, Victoria University of Wellington, New Zealand
2Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand

Copyright © 2004 Dong Qian Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The interaction between linear, quadratic programming and regression analysis are explored by both statistical and operations research methods. Estimation and optimization problems are formulated in two different ways: on one hand linear and quadratic programming problems are formulated and solved by statistical methods, and on the other hand the solution of the linear regression model with constraints makes use of the simplex methods of linear or quadratic programming. Examples are given to illustrate the ideas.