Predicting Student’s Campus Placement Probability using Binary Logistic Regression
D. Satish Kumar1, Zailan Bin Siri2, D.S. Rao3, S. Anusha4

1D. Satish Kumar, Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram- 522502, India.
2Zailan Bin Siri, Institute of Mathematical Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia.
3D.S. Rao, Business School, Koneru Lakshmaiah Education Foundation, Vaddeswaram- 522502, India.
4S. Anusha, Department of Mathematics, Koneru Lakshmaiah Education Foundation, Vaddeswaram- 522502, India.

Manuscript received on 30 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 2633-2635 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8984078919/19©BEIESP | DOI: 10.35940/ijitee.I8984.078919
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Abstract: 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.
Keywords: Campus Placements Technical Education Odds Ratio Binary Logistic Regression Goodness of Fit Confusion Matrix

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