Acute Performance Estimation of Students Using Quantile Regression Approach (A Case Study of Lahore)

Sunaina Ishtiaq, Yasar Mahmood, Dr. Hina Khan

Abstract


Extreme behavior (Performance) of students is inclined by number of factor which must be painted for important policy implications. This study states that the CGPA is the most important system to deduct student performance. Data on CGPA has been collected from B.A/B.Sc (Hons.) of 32 private and public universities of Lahore. Generally, researchers investigate an average performance of the students with classical methods of simple linear regression. This approach does not give complete picture of different variables influencing student performance from corner to corner. Quantile regression introduces information across the whole distribution of the student’s achievements. Study furnishes that students performance strongly affected by father’s education. Student’s gender, passion for fashion, and mother’s job are significant factors. Class participation is found as a magical variable that has positive impact on student performance at all quantiles. The quantile estimate of student performance shows that effect of the urban-rural difference is significant factor. The study clearly shows for high performance students, factors like mother occupation, father education, gender and area become insignificant at high quantiles. The results highlight that quantile regression model is a useful technique for examine information than ordinary least squares. It also depicts that ordinary least squares underestimated and overestimated the Quantile regression at different quantiles.


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DOI: http://dx.doi.org/10.30971/pje.v36i3.850

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