Open AccessPredicting Student’s Campus Placement Probability using Binary Logistic Regression
Author(s) -
Dinesh Kumar,
Zailan Siri,
D. S. Prasada Rao,
S. Anusha
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i8984.078919
Subject(s) - logistic regression , context (archaeology) , reputation , odds , sample (material) , identification (biology) , medical education , mathematics education , computer science , psychology , statistics , sociology , medicine , mathematics , geography , social science , chemistry , botany , archaeology , chromatography , biology
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.