Authors
Nizamitdinov H.I. – Doctor of Philosophy, Department of Programming and Information Technologies, Polytechnic Institute of Tajik Technical University.
Inomov B.B. – Assistant, Department of Programming and Information Technologies,Polytechnic Institute of Tajik Technical University.
Annotation
The article considered statistical analysis and the construction of a theoretical model of exam results using non-parametric regression models. A literature review is conducted to identify the most frequently used methods for evaluating the functions of the multivariate dependence of the exam results. These types of models include approximation methods using spline functions, penalized splines and regression splines. It was concluded that the methods for estimating non-linear dependencies between variables can also be used in finding the relationship between the factors influencing the quality of education. Examples of such tasks can be the determination of the relationship between the informative features of syllabus and suggested literatures, syllabus and examination tests.
Key words
Statistics, regression methods, additive models, spline functions, exam result.
Language english |
Type technical |
Year 2018 |
Page 11 |
References
- Arafiev V.P., Mikhalchuk A.A. Computer statistical analysis of the quality of engineering education. News of Tomsk Polytechnic University. 2005. T. 308. 4. 226 – 231.
- Mikhalchuk A.A., Arafev V.P., Filipenko N.M. Comparative statistical analysis of parametric and non-parametric methods of knowledge assessment in the system of distance learning. Modern problems of science and education. – 2013. – № 3.
- Goutam Saha. Applying logistic regression model data. Journal of Reliability and Statistical Studies. 2011. 4. 105 – 117.
- Hastie T.J. and Tibshirani R.J. Generalized Additive Models. Chapman & Hall / CRC, 1990.
- Wood S.N. Generalized additive models: an introduction with R, Chapman and Hall, 2006.
- Eilers P.H.C. and Marx B.D., Direct generalized rule, for example, Computational Statistics and Data Analysis, 28, P. 193 – 209, 1998.
- Eilers P.H.C. and Marx B.D., Flexible smoothing using re-links likelihood (with comments and rejoinders), Statistical Science, 11 (2), P. 89 – 121, 1996.
- Silvia Figini, Paolo Giudici. Statistical models for e-learning data. 18 (2): 293-304 · July 2009.
Publication date
2023-09-25