A Probabilistic Model for Predicting Examination Performance: A Binary Time Series Regression Approach

Authors

  • I. U. MOFFAT

Keywords:

Binary, binomial regression, logit, probability prediction, time series.

Abstract

In any examination result, performance is a function of several variables, which could be linear or nonlinear in dimension.

References

. Agresti, A. An Introduction to categorical data analysis, 2007, 423, Wiley

. Long, J. S. Regression models for Categorical and Limited Dependent Variables. Advanced Quantitative Techniques in the Social Sciences, 1997, 7. Sage Publications: Thousand Oaks, CA.

. Powers, D., and Xie, Y. Statistical method for categorical data analysis Academic Press

. Melton, J.

. Han, M., and Michael, A. G. Multiple Regression Analysis and Mass Assessment: A Review of the Issues. The Appraisal Journal, 2001, 1, pp. 89 - 109.

. Hastie, T. and Tibshirani, R. Generalized additive models (2nd Edition), 1990, London: Chapman & Hall.

. Aldrich, J. H., and Nelson, F. D. Linear Probability, Logit, and Probit Models. John Herbert Aldrich Quantitative Applications in the Social Sciences, 1984, 45.

. Strano, M., and Colosimo, B. M. Logistic regression analysis for experimental determination of forming limit diagrams. International Journal of Machine Tools and Manufacture, 2006, pp. 46

Downloads

Published

2015-02-21

How to Cite

MOFFAT, I. U. (2015). A Probabilistic Model for Predicting Examination Performance: A Binary Time Series Regression Approach. International Journal of Sciences: Basic and Applied Research (IJSBAR), 16(2), 375–394. Retrieved from https://gssrr.org/index.php/JournalOfBasicAndApplied/article/view/3469

Issue

Vol. 16 No. 2 (2014)

Section

Articles

License

Authors who submit papers with this journal agree to the following terms.