Binary Logistic Regression Analysis on Predicting Academics Performance
| Autor: | J. Brakoru, O. A. Ogunleye, E. K. Akinyemi, H.O Olaoye |
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| Přispěvatelé: | Federal School of Statistics |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Current Journal of Applied Science and Technology Current Journal of Applied Science and Technology, Current Journal of Applied Science and Technology, 2021, pp.1-6. ⟨10.9734/cjast/2021/v40i2031458⟩ |
| ISSN: | 2457-1024 |
| DOI: | 10.9734/cjast/2021/v40i2031458⟩ |
| Popis: | International audience; This paper considers the application of logistic regression model to predict academics performance of students. The choice of this model becomes imperative as a result of dichotomous relationship existing in the model (either pass or fail). 100 students from the four department where engaged in the study. Statistical package for social scientist (SPSS) was used for the analysis. The results show that monthly allowance of students, and study time of the students were significant predictors. While gender and educational level of parent were insignificant predictors. The fitness of the model was assessed using Hosmer and Lemeshow test, split-sample approach and other supplementary indices to validate the model. The fitted model indicated that fitted binary logistic regression model could be used to predict the future performance of students. |
| Databáze: | OpenAIRE |
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