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@inproceedings{7939, author = {Berka, Petr and Marek, Lubos and Vrabec, Michal}, address = {Paris}, booktitle = {Proceedings of 22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics}, doi = {http://dx.doi.org/10.2991/amse-19.2019.8}, editor = {Boda, M., Mendelova, V., Grausova, M.}, keywords = {student dropout; logistic regression; decision trees; association rules}, howpublished = {elektronická verze "online"}, language = {eng}, location = {Paris}, isbn = {978-94-6252-804-8}, pages = {70-80}, publisher = {Atlantis Press}, title = {Modeling Students Dropout Using Statistical and Data Mining Methods}, url = {https://www.atlantis-press.com/proceedings/amse-19/125919265}, year = {2019} }
TY - JOUR ID - 7939 AU - Berka, Petr - Marek, Lubos - Vrabec, Michal PY - 2019 TI - Modeling Students Dropout Using Statistical and Data Mining Methods PB - Atlantis Press CY - Paris SN - 9789462528048 KW - student dropout KW - logistic regression KW - decision trees KW - association rules UR - https://www.atlantis-press.com/proceedings/amse-19/125919265 L2 - https://www.atlantis-press.com/proceedings/amse-19/125919265 N2 - Not completing the study by a large portion of students is a serious problem at the universities worldwide. Regardless of the countries, the numbers are very similar: about one-half of students who enrolled for the bachelor study leave the university before obtaining the degree. To deal with this problem we create models to distinguish between students who successfully completed their study and students who dropped out of the university. Models created using traditional statistical analysis techniques (logistic regression) are compared with models created using data mining methods (decision trees, rules). ER -
BERKA, Petr, Lubos MAREK and Michal VRABEC. Modeling Students Dropout Using Statistical and Data Mining Methods. In Boda, M., Mendelova, V., Grausova, M. \textit{Proceedings of 22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics}. Paris: Atlantis Press. p.~70-80. ISBN~978-94-6252-804-8. doi:10.2991/amse-19.2019.8. 2019.
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