Research on the Admission of Graduate Students Based on Multiple Regression Model

DOI

10.2991/978-94-6463-042-8_98How to use a DOI?

Keywords

Graduate admission; Multiple linear regression; Model selection; Model diagnostic

Abstract

In light of the unprecedented high unemployment rate due to the latest spread and development of coronavirus with only a few available internship and full-time job opportunities for college graduates, this research based on large sample post-graduate admission statistics dataset and prospective post-graduates applicants’ perspective mainly adopted sequential variable selection methods to generate finite number of ordinary least square multiple linear regression models, utilized best subset model selection framework in terms of comparing corresponding types of evaluation metric and criterion, incorporated various model diagnostic plots to validate important regression model assumptions and constructed an cube-transformation technique to correct the heteroskedasticity presented in the model. This research concluded that selected optimal ordinary least squared multiple linear regression models under cubed transformation could achieve satisfactory performance of forecasting the post-graduate admission chance without sacrificing the goodness of fit. This research helps post-graduate applicant to understand importance of each admission covariate contributed to overall admission chance.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bo Liu
PY  - 2022
DA  - 2022/12/29
TI  - Research on the Admission of Graduate Students Based on Multiple Regression Model
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 681
EP  - 689
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_98
DO  - 10.2991/978-94-6463-042-8_98
ID  - Liu2022
ER  -