Using data mining techniques to identify “the best” operational patterns for enrollment modeling
| dc.contributor.author | Obarse, Bogdan Catalin | |
| dc.contributor.committeeChair | Hamner, Mark S. | |
| dc.contributor.committeeMember | Marshall, David, Ph. D. | |
| dc.contributor.committeeMember | Grigorieva, Ellina | |
| dc.date.accessioned | 2018-10-24T16:21:42Z | |
| dc.date.available | 2018-10-24T16:21:42Z | |
| dc.date.issued | 2009-08 | |
| dc.description.abstract | For any Educational Institution it is very important to know the number of new students and the number of returning students. Based on these numbers, there could be conducted predictions of the budget that the institution will have for the next year. This research will utilize pre-existing historical data from Texas Woman's University containing readily available and easily measured factors, which most institutions of higher learning will have available, and will split the existing data in all the sub sets possible. Running a chi square analysis on each set obtained, the program will be able to show us which splitting way is better for obtaining the most consistent patterns, using the provided data. The results will be compared with the results obtained running a linear regression analysis on the same data sets. The study will introduce an extraneous hidden-time variable related to partitioning ways possible. The program can be used in the future on any University data sets, providing the most holding combination of variables that will hold over the years. | en_US |
| dc.identifier.uri | https://hdl.handle.net/11274/10586 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Education | en_US |
| dc.subject | Applied sciences | en_US |
| dc.subject | Pure sciences | en_US |
| dc.title | Using data mining techniques to identify “the best” operational patterns for enrollment modeling | en_US |
| dc.type | Thesis | en_US |
| thesis.degree.college | College of Arts and Sciences | |
| thesis.degree.discipline | Mathematics | |
| thesis.degree.grantor | Texas Woman's University | en_US |
| thesis.degree.level | Master | en_US |
| thesis.degree.name | Master of Science | en_US |