Construction of Teaching Reform Model of International Trade Based on Correlation Analysis and Regression Analysis in SPSS Statistical Software
Authors
Wang Aiqin1, Yuan Qingyuan2, *
1School of Economics and Management, Taishan University, Taian, Shandong, China
2Shandong University of Science and Technology, Taian, Shandong, China
*Corresponding author.
Email: 58316772@qq.com
Corresponding Author
Yuan Qingyuan
Available Online 27 December 2022.
- DOI
- 10.2991/978-94-6463-044-2_79How to use a DOI?
- Keywords
- Correlation Analysis; Regression Analysis; Teaching Reform Model
- Abstract
This paper makes a descriptive statistical analysis of the data through SPSS statistical software, and constructs a model of international trade teaching reform from four aspects: teaching concept reform, reform of teaching methods, integrate ideological and political elements, and construction of curriculum supporting. The model is verified by using the correlation analysis and regression analysis in SPSS software, and finally points out the key problems to be solved in the reform of international trade teaching.
- 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 - Wang Aiqin AU - Yuan Qingyuan PY - 2022 DA - 2022/12/27 TI - Construction of Teaching Reform Model of International Trade Based on Correlation Analysis and Regression Analysis in SPSS Statistical Software BT - Proceedings of the 2022 3rd International Conference on Modern Education and Information Management (ICMEIM 2022) PB - Atlantis Press SP - 620 EP - 628 SN - 2667-128X UR - https://doi.org/10.2991/978-94-6463-044-2_79 DO - 10.2991/978-94-6463-044-2_79 ID - Aiqin2022 ER -