Predicting student’s campus placement probability using binary logistic regression
Students aspiring for technical education generally select educational institutions with good track record in campus placements. Many a times the reputation of such institute is determined by the pay packages offered by recruiters to its students. In this context it is pertinent to investigate and i...
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| Format: | Article |
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Blue Eyes Intelligence Engineering & Sciences Publication
2019
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| _version_ | 1825722073477021696 |
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| author | Kumar, D. Satish Siri, Zailan Rao, D.S. Anusha, S. |
| author_facet | Kumar, D. Satish Siri, Zailan Rao, D.S. Anusha, S. |
| author_sort | Kumar, D. Satish |
| collection | UM |
| description | Students aspiring for technical education generally select educational institutions with good track record in campus placements. Many a times the reputation of such institute is determined by the pay packages offered by recruiters to its students. In this context it is pertinent to investigate and identify those factors that may influence the student campus placement chances in technical education. The State of Andhra Pradesh which has a high concentration of technical education institutes was chosen as the study area. A careful review of literature lead to the identification of six hypothetical determinants of student campus placement in technical education. A random sample 250 MBA student’s placement data were gathered from different institutes and six predictor binary logistic regression model was fitted to the data to estimate the odds for the student campus placement. Estimated Results of the study indicate that the chances of campus placement is influenced by four predictors: CGPA, Specialization in PG, Specialization in UG and Gender. © BEIESP. |
| first_indexed | 2024-03-06T06:00:04Z |
| format | Article |
| id | um.eprints-23513 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:00:04Z |
| publishDate | 2019 |
| publisher | Blue Eyes Intelligence Engineering & Sciences Publication |
| record_format | dspace |
| spelling | um.eprints-235132020-01-21T07:40:10Z http://eprints.um.edu.my/23513/ Predicting student’s campus placement probability using binary logistic regression Kumar, D. Satish Siri, Zailan Rao, D.S. Anusha, S. Q Science (General) QA Mathematics Students aspiring for technical education generally select educational institutions with good track record in campus placements. Many a times the reputation of such institute is determined by the pay packages offered by recruiters to its students. In this context it is pertinent to investigate and identify those factors that may influence the student campus placement chances in technical education. The State of Andhra Pradesh which has a high concentration of technical education institutes was chosen as the study area. A careful review of literature lead to the identification of six hypothetical determinants of student campus placement in technical education. A random sample 250 MBA student’s placement data were gathered from different institutes and six predictor binary logistic regression model was fitted to the data to estimate the odds for the student campus placement. Estimated Results of the study indicate that the chances of campus placement is influenced by four predictors: CGPA, Specialization in PG, Specialization in UG and Gender. © BEIESP. Blue Eyes Intelligence Engineering & Sciences Publication 2019 Article PeerReviewed Kumar, D. Satish and Siri, Zailan and Rao, D.S. and Anusha, S. (2019) Predicting student’s campus placement probability using binary logistic regression. International Journal of Innovative Technology and Exploring Engineering, 8 (9). pp. 2633-2635. ISSN 2278-3075, https://www.ijitee.org/wp-content/uploads/papers/v8i9/I8984078919.pdf |
| spellingShingle | Q Science (General) QA Mathematics Kumar, D. Satish Siri, Zailan Rao, D.S. Anusha, S. Predicting student’s campus placement probability using binary logistic regression |
| title | Predicting student’s campus placement probability using binary logistic regression |
| title_full | Predicting student’s campus placement probability using binary logistic regression |
| title_fullStr | Predicting student’s campus placement probability using binary logistic regression |
| title_full_unstemmed | Predicting student’s campus placement probability using binary logistic regression |
| title_short | Predicting student’s campus placement probability using binary logistic regression |
| title_sort | predicting student s campus placement probability using binary logistic regression |
| topic | Q Science (General) QA Mathematics |
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