Research Article

Predicting the Possibility of Student Admission into Graduate Admission by Regression Model: A Statistical Analysis

Authors

  • Ashiqul Haque Ahmed Economics & Decision Science, University of South Dakota, South Dakota, USA
  • Sabbir Ahmad Department of Mathematical Science University of South Dakota South Dakota, USA
  • Md Abu Sayed Department of Professional Security Studies, New Jersey City University, Jersey City, New Jersey, USA
  • Malay sarkar Department of Management Science and Quantitative Methods, Gannon University, USA
  • Eftekhar Hossain Ayon Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
  • Md Tuhin Mia School of Business, International American University, Los Angeles, California, USA
  • Tahera Koli Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA
  • Rumana Shahid Department of Management of Science and Quantitative Methods, Gannon University, USA

Abstract

This study aims to alleviate the uncertainties faced by prospective students during the application process by developing a predictive model for admission probabilities based on CGPA and GRE scores. The research investigates the significance of these predictor variables about the response variable, "Chance of Admit." Employing linear regression analysis, the model is thoroughly examined to evaluate its adequacy, predictive accuracy, and the need for interaction terms. The findings indicate that both CGPA and GRE scores play a crucial role in forecasting admission chances, with an adjusted R2 value of 0.0835, suggesting an 80% reduction in variance around the regression compared to the main line. The diagnostic plot of the model confirms its precision, revealing minimal deviations from linearity and normality in residuals. Furthermore, the study addresses concerns about multicollinearity using the Variable Inflation Factor (VIF) and finds no significant correlation between GRE Scores and CGPA. In summary, this research presents a robust predictive model for student admission probabilities, offering valuable insights for both prospective applicants and educational institutions.

Article information

Journal

Journal of Mathematics and Statistics Studies

Volume (Issue)

4 (4)

Pages

97-105

Published

2023-11-27

How to Cite

Predicting the Possibility of Student Admission into Graduate Admission by Regression Model: A Statistical Analysis. (2023). Journal of Mathematics and Statistics Studies, 4(4), 97-105. https://doi.org/10.32996/jmss.2023.4.4.10

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Keywords:

Linear Regression, Predictive Model, Variable Inflation Factor, Adjusted R2

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