Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

New Approach in Fitting Linear Regression Models with the Aim of Improving Accuracy and Power

Document Type : Research Paper

Authors
Soheil Sadeghi
Hashem Mahlooji
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
Abstract
The main contribution of this work lies in challenging the common practice of inferential statistics in the realm of simple linear regression for attaining a higher degree of accuracy when multiple observations are available, at least, at one level of the regressor variable. We derive sufficient conditions under which one can improve the accuracy of the interval estimations at quite affordable extra computational cost. Two algorithms and a numerical example will be presented to fully explain how our approach works and to compare the results of our approach versus the results obtained from three of the well known statistical software systems.
Keywords
Simple linear regression
Multiple observations
Weighted least squares
accuracy
Power
Subjects
[1] Montgomery D.C., Peck E.A., Vining G.G. (2001), Introduction to Linear Regression Analysis; 3rd
edition, Wiley.
[2] Neter J., Kutner M.H., Nachtsheim C.J., Wasserman W. (1996), Applied Linear Regression Models;
3rd edition, Irwin.
[3] MINITAB® Release 14.12.0, http://www.minitab.com, September 2010.
[4] R 2.11.1 system, http://www.r-project.org, September 2010.
[5] SAS 9.1 (Statistical Analysis System), http://www.sas.com, September 2010.
Journal of Industrial and Systems Engineering
Volume 4, Issue 2 - Serial Number 2
Summer 2010
Pages 95-113

  • Receive Date 04 May 2009
  • Revise Date 18 September 2009
  • Accept Date 09 January 2010