Statistical Assessment of the Relationship Between Subject Performance and CGPA

Abstract

This study investigates the relationship between course performance in core mathematics subjects and overall academic achievement measured through Cumulative Grade Point Average (CGPA). The dataset consists of 50 students’ academic records collected from the official marksheet portal of the University of Sindh (7th and 8th semester results). The dependent variable  represents students’ combined CGPA of the 7th and 8th semesters, while the independent variables include the marks obtained in Functional Analysis , Numerical Analysis-II , and Rings and Fields . A multiple linear regression model is applied to examine how effectively these three subjects predict overall semester CGPA. The analysis aims to determine the strength of association, contribution of each subject to academic performance, and the predictive capability of the regression model. Statistical measures such as , , Adjusted , standard errors, and t-statistics are used to evaluate model significance and goodness of fit. The findings of this study provide insight into the academic impact of advanced mathematics courses on overall student performance. The results may assist academic planners and educators in identifying key subjects that significantly influence CGPA and help in curriculum evaluation and academic performance forecasting.