Proceedings of the 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)

A study on the relationship between students' course scores and starting wages based on principal component regression

Authors
Yi Yang, Yan Song
Corresponding Author
Yi Yang
Available Online December 2017.
DOI
https://doi.org/10.2991/anit-17.2018.48How to use a DOI?
Keywords
course score, starting wage, principal component regression analysis.
Abstract
To accurately evaluate students' professional ability, optimize their curriculum, principal component regression analysis method is used to design the principal component regression model about the relationship between course scores and starting wages . Some empirical analysis is made based on score data of students which majored computer science and technology in Hunan women's university. The experiment result shows that model can more accurately describe the relationship between course scores and starting wages , which can be used as a tool to evaluate students' professional ability, forecast analysis on the employment situation for students.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
December 2017
ISBN
978-94-6252-447-7
ISSN
1951-6851
DOI
https://doi.org/10.2991/anit-17.2018.48How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yi Yang
AU  - Yan Song
PY  - 2017/12
DA  - 2017/12
TI  - A study on the relationship between students' course scores and starting wages based on principal component regression
BT  - 2017 International Seminar on Artificial Intelligence, Networking and Information Technology (ANIT 2017)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/anit-17.2018.48
DO  - https://doi.org/10.2991/anit-17.2018.48
ID  - Yang2017/12
ER  -