Comparison of Logistic Regression and Generalized Linear Model for Identifying Accurate At – Risk Students

Abstract

Aim: To predict the accuracy percentage of At - risk students based on High withdrawal and Failure rate. Materials and methods: Logistic Regression with sample size = 20 and Generalised Linear Model (GLM) with sample size = 20 was iterated different times for predicting accuracy percentage of At - risk students. The Novel sigmoid function used in Logistic Regression maps prediction to probabilities which helps to improve the prediction of accuracy percentage. Results and Discussion: Logistic Regression has significantly better accuracy (94.48 %) compared to GLM accuracy (92.76 %). There was a statistical significance between Logistic regression and GLM (p=0.000) (p<0.05). Conclusion: Logistic Regression with Novel Sigmoid function helps in predicting with more accuracy percentage of At - risk students.