Consequences de la sélection de variables sur l'interprétation des résultats en régression linéaire multiple

Consequences of variable selection on the interpretation of the results in multiple linear regression. A priori or a posteriori variable selection is a common practise in multiple linear regression. The user is however not always aware of the consequences on the results due to this variable select...

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Main Authors: Palm R., Akossou AYJ.
Format: Article
Language:English
Published: Presses Agronomiques de Gembloux 2005-01-01
Series:Biotechnologie, Agronomie, Société et Environnement
Subjects:
Online Access:http://www.pressesagro.be/base/text/v9n1/11.pdf
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author Palm R.
Akossou AYJ.
author_facet Palm R.
Akossou AYJ.
author_sort Palm R.
collection DOAJ
description Consequences of variable selection on the interpretation of the results in multiple linear regression. A priori or a posteriori variable selection is a common practise in multiple linear regression. The user is however not always aware of the consequences on the results due to this variable selection. In this note, the presence of omission bias and selection bias is explained by means of a Monte Carlo experiment. The consequences of variable selection on the regression coefficients and on the predicted values are then analysed. The user’s attention is drawn to the risk of misinterpretation of the regression coefficients, specially after variable selection. On the other hand, the consequences of variable selection on the predicted values of the response variable are rather limited, at least for the given example.
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spelling doaj.art-1f553b5b95d24be5b5d51c37f3de056b2022-12-21T19:03:30ZengPresses Agronomiques de GemblouxBiotechnologie, Agronomie, Société et Environnement1370-62331780-45072005-01-01911118Consequences de la sélection de variables sur l'interprétation des résultats en régression linéaire multiplePalm R.Akossou AYJ.Consequences of variable selection on the interpretation of the results in multiple linear regression. A priori or a posteriori variable selection is a common practise in multiple linear regression. The user is however not always aware of the consequences on the results due to this variable selection. In this note, the presence of omission bias and selection bias is explained by means of a Monte Carlo experiment. The consequences of variable selection on the regression coefficients and on the predicted values are then analysed. The user’s attention is drawn to the risk of misinterpretation of the regression coefficients, specially after variable selection. On the other hand, the consequences of variable selection on the predicted values of the response variable are rather limited, at least for the given example.http://www.pressesagro.be/base/text/v9n1/11.pdfRegressionvariable selectionomission biasselection biassimulationstatistical method
spellingShingle Palm R.
Akossou AYJ.
Consequences de la sélection de variables sur l'interprétation des résultats en régression linéaire multiple
Biotechnologie, Agronomie, Société et Environnement
Regression
variable selection
omission bias
selection bias
simulation
statistical method
title Consequences de la sélection de variables sur l'interprétation des résultats en régression linéaire multiple
title_full Consequences de la sélection de variables sur l'interprétation des résultats en régression linéaire multiple
title_fullStr Consequences de la sélection de variables sur l'interprétation des résultats en régression linéaire multiple
title_full_unstemmed Consequences de la sélection de variables sur l'interprétation des résultats en régression linéaire multiple
title_short Consequences de la sélection de variables sur l'interprétation des résultats en régression linéaire multiple
title_sort consequences de la selection de variables sur l interpretation des resultats en regression lineaire multiple
topic Regression
variable selection
omission bias
selection bias
simulation
statistical method
url http://www.pressesagro.be/base/text/v9n1/11.pdf
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