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International Journal of Academic Research in Progressive Education and Development

Open Access Journal

ISSN: 2226-6348

Multiple Regression in Determining Affecting Factors Student Success in a Statistics Subject

Nur Syuhada Muhammat Pazil, Norwaziah Mahmud

http://dx.doi.org/10.6007/IJARPED/v12-i3/17410

Open access

This study aims to find the factor that affects students’ success in Introductory Statistics Subjects based on a Multiple Linear Regression (MLR). Gender and assessment achievements such as test 1, test 2, quiz, assignment, group project, and final test marks were investigated as predictors. The dependent variable is the overall marks of the subject. The results shows that test 1, test 2, assignment, project, and final test have a significant difference to the overall marks of the statistics subject. This study was carried out using SPSS software. In order to determine the significant variables, further research can be done using more sample size and more variables.

Daoud, J. I. (2018). Multicollinearity and Regression Analysis. Journal of Physics: Conference Series, 949(1). https://doi.org/10.1088/1742-6596/949/1/012009
Dhakal, C. P. (2018). Multiple Linear Regression in SPSS. Article in International Journal of Science and Research. https://www.researchgate.net/publication/333973273
Forson, I. K., & Vuopala, E. (2019). Online learning readiness: perspective of students enrolled in distance education in Ghana. The Online Journal of Distance Education and E-Learning, 7(4), 277–294. www.tojdel.net
Hsu Wang, F. (2019). On prediction of online behaviors and achievement using self-regulated learning awareness in flipped classrooms. International Journal of Information and Education Technology, 9(12), 874–879. https://doi.org/10.18178/ijiet.2019.9.12.1320
Klees, S. J. (2016). Inferences from regression analysis: are they valid? Real-World Economics Review, 74, 85–97.
Mahmud, N., Pazil, M. N. S., & Azman, N. A. N. (2022). The significant factors affecting students’ academic performance in online class: Multiple linear regression approach. Jurnal Intelek, 17(2), 1–11. https://doi.org/10.24191/ji.v17i2.17896
Rachmawati, S., Mutrofin, & Sumardi. (2021). The effect of online learning and parental guidance towards the result of XI social students’ learning on Geography course at SMAN 5 Jember. IOP Conference Series: Earth and Environmental Science, 747(1). https://doi.org/10.1088/1755-1315/747/1/012028
Shedriko. (2021). Binary logistic regression in determining affecting factors student graduation in a subject. Jurnal Teknologi Dan Open Source, 4(1), 114–120. https://doi.org/10.36378/jtos.v4i1.1401
Syuhada, N., Pazil, M., Mahmud, N., Baharom, N., & Hafawati, S. (2023). Logistic regression in determining affecting factors student success in an introductory statistics subject. Jurnal Intelek, 18(1), 9–16. https://doi.org/https://doi.org/10.24191/ji.v18i1.20133
Omolewa, T. O., Oladele, T. A., Adeyinka, A., & Oluwaseun, R. O. (2019). Prediction of student’s academic performance using k-eans clustering and multiple linear regressions. Journal of Engineering and Applied Sciences, 14(22), 8254–8260. https://doi.org/10.36478/jeasci.2019.8254.8260
Weidlich, J., & Bastiaens, T. J. (2018). Technology matters - the impact of transactional distance on satisfaction in online distance learning. International Review of Research in Open and Distance Learning, 19(3), 222–242.
https://doi.org/10.19173/irrodl.v19i3.3417
Yang, S. J. H., Lu, O. H. T., Huang, A. Y. Q., Huang, J. C. H., Ogata, H., & Lin, A. J. Q. (2018). Predicting students’ academic performance using multiple linear regression and principal component analysis. Journal of Information Processing, 26, 170–176. https://doi.org/10.2197/ipsjjip.26.170


In-Text Citation: (Pazil & Mahmud, 2023)
To Cite this Article: Pazil, N. S. M., & Mahmud, N. (2023). Multiple Regression in Determining Affecting Factors Student Success in a Statistics Subject. International Journal of Academic Research in Progressive Education and Development, 12(3), 492–499.