Comparing least-squares and goal programming estimates of linear regression parameter.

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...

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Main Authors: Ahmad, Maizah Hura, Adnan, Robiah, Lau, Chik Kong, Mohd. Daud, Zalina
Format: Article
Language:English
Published: Department of Mathematics, Faculty of Science 2005
Subjects:
Online Access:http://eprints.utm.my/8795/
http://eprints.utm.my/8795/1/MaizahHuraAhmad2005_ComparingLeast-SquaresandGoalProgramming.pdf
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author Ahmad, Maizah Hura
Adnan, Robiah
Lau, Chik Kong
Mohd. Daud, Zalina
author_facet Ahmad, Maizah Hura
Adnan, Robiah
Lau, Chik Kong
Mohd. Daud, Zalina
author_sort Ahmad, Maizah Hura
building UTeM Institutional Repository
collection Online Access
description 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.
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publishDate 2005
publisher Department of Mathematics, Faculty of Science
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spelling utm-87952017-10-11T01:54:47Z http://eprints.utm.my/8795/ Comparing least-squares and goal programming estimates of linear regression parameter. Ahmad, Maizah Hura Adnan, Robiah Lau, Chik Kong Mohd. Daud, Zalina QA Mathematics 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. Department of Mathematics, Faculty of Science 2005-12 Article PeerReviewed application/pdf en http://eprints.utm.my/8795/1/MaizahHuraAhmad2005_ComparingLeast-SquaresandGoalProgramming.pdf Ahmad, Maizah Hura and Adnan, Robiah and Lau, Chik Kong and Mohd. Daud, Zalina (2005) Comparing least-squares and goal programming estimates of linear regression parameter. Matematika, 21 (2). pp. 101-112. ISSN 0127-8274
spellingShingle QA Mathematics
Ahmad, Maizah Hura
Adnan, Robiah
Lau, Chik Kong
Mohd. Daud, Zalina
Comparing least-squares and goal programming estimates of linear regression parameter.
title Comparing least-squares and goal programming estimates of linear regression parameter.
title_full Comparing least-squares and goal programming estimates of linear regression parameter.
title_fullStr Comparing least-squares and goal programming estimates of linear regression parameter.
title_full_unstemmed Comparing least-squares and goal programming estimates of linear regression parameter.
title_short Comparing least-squares and goal programming estimates of linear regression parameter.
title_sort comparing least-squares and goal programming estimates of linear regression parameter.
topic QA Mathematics
url http://eprints.utm.my/8795/
http://eprints.utm.my/8795/1/MaizahHuraAhmad2005_ComparingLeast-SquaresandGoalProgramming.pdf