Based on the Principal Component Analysis and Ridge Regression Equation to Explore the Influencing Factors of English Ability in Chengdu Technological University, China
- DOI
- 10.2991/978-94-6463-044-2_92How to use a DOI?
- Keywords
- Evaluation model; Statistical modeling; English ability; Principal component analysis; Ridge regression
- Abstract
In order to explore the evaluation model of college students’ English ability, the questionnaire survey in this paper data statistics on interest in English reading, grammatical analysis ability, reading skills, etc. Then this paper uses Principal Component Analysis (PCA) to statistical modeling and analyzes the English ability of students in Chengdu Technological University, China. Using Ridge Regression to explore and analyze the factors that affect English grade, and establish a prediction model of variables and performance. This article aims to provide new ideas and methods for teachers teaching in different levels of classes and for non-native English-speaking students in the process of learning English.
- Copyright
- © 2022 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.
Cite this article
TY - CONF AU - Ziqian Liu AU - Feilong Qin AU - Zheng Zeng AU - Shilin Wang AU - Jie He AU - Ke Wang AU - Liuli Lu PY - 2022 DA - 2022/12/27 TI - Based on the Principal Component Analysis and Ridge Regression Equation to Explore the Influencing Factors of English Ability in Chengdu Technological University, China BT - Proceedings of the 2022 3rd International Conference on Modern Education and Information Management (ICMEIM 2022) PB - Atlantis Press SP - 729 EP - 739 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-044-2_92 DO - 10.2991/978-94-6463-044-2_92 ID - Liu2022 ER -