Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis
American Journal of Theoretical and Applied Statistics
Volume 4, Issue 5, September 2015, Pages: 414-419
Received: Jul. 25, 2015;
Accepted: Aug. 6, 2015;
Published: Sep. 18, 2015
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Akinwande Michael Olusegun, Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
Hussaini Garba Dikko, Department of Mathematics, Ahmadu Bello University, Zaria, Nigeria
Shehu Usman Gulumbe, Department of Mathematics, Usman Danfodiyo University, Sokoto, Nigeria
In application, one major difficulty a researcher may face in fitting a multiple regression is the problem of selecting significant relevant variables, especially when there are many independent variables to select from as well as having in mind the principle of parsimony; a comparative study of the limitation of stepwise selection for selecting variables in multiple regression analysis was carried out. Regression analysis in its bi-variate and multiple cases and stepwise selection (forward selection, backward elimination and stepwise selection) was employed for this study comparing the zero-order correlations and Beta (β) weights to give a clearer picture of the limitation of stepwise selection. Subsequently, from the comparisons, it was evident that including the suspected predictor (suppressor) variable that was not significant in the bi-variate case as suggested by the stepwise selection improved the beta weight of other predictors in the model and the overall predictability of the model as argued.
Akinwande Michael Olusegun,
Hussaini Garba Dikko,
Shehu Usman Gulumbe,
Identifying the Limitation of Stepwise Selection for Variable Selection in Regression Analysis, American Journal of Theoretical and Applied Statistics.
Vol. 4, No. 5,
2015, pp. 414-419.
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