Evaluating Prediction Factor Prominence in Academic Domain Selection using Dominance Analysis – Ministry of Higher Education (MoHE), Ibri CAS, Sultanate of Oman
S. Sharmi1, Ishtiaque Mahmood2, Jehad Bani-Younis3
1Ms. S. Sharmi, Department of IT, IBRI College of Applied Sciences, Sultanate of Oman.
2Ms. Ishtiaque Mahmood, Department of IT, IBRI College of Applied Sciences, Sultanate of Oman.
3Dr. Jehad Bani-Younis, Department of IT, IBRI College of Applied Sciences, Sultanate of Oman.
Manuscript received on January 06, 2015. | Revised Manuscript received on January 20, 2015. | Manuscript published on February 28, 2015. | PP: 18-21 | Volume-4 Issue-3, February 2015. | Retrieval Number: C3701024315/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper, advocates on a broader use of relative prominence keys as an appendage to multiple regression analysis. The goal of such analysis is to screen the variance among multiple predictors to realize the role played by each predictor in a regression equation. Dominance Analysis is a method to evaluate the relative prominence of the prognosticators. Regrettably, when predictors are correlated, they totally trust on metrics which are flawed indicators of variable importance. Furthermore, the key benefits of two relative prominence analyses, dominance analysis and relative weight analysis, over estimates produced by multiple regression analysis. Here, this investigation helps us to evaluate the importance of the prediction factors involved in determining the criteria’s for domain selection of the students. A mockup study was conducted to evaluate the performance of the proposed actions and develop commendations.
Keywords: Predictor prominence, Weight analysis, Dominance Analysis (DA), Multiple Linear Regression (MLR).