Analysis of Influencing Factors of Human Resource Salary Based on Anova Analysis
DOI: https://doi.org/10.62517/jbm.202509102
Author(s)
Faye Wang*
Affiliation(s)
School of Business, Shandong Normal University, Jinan, China
*Corresponding Author.
Abstract
The main purpose of this report is to explore the key factors affecting the salary level of human resources. Through the analysis of variance (ANOVA) and linear regression analysis, this paper analyzes the influence of enterprise nature, enterprise scale, experience interval, academic requirements and city on the salary level. The results show that the nature of the enterprise, the size of the enterprise, experience, educational requirements and regional factors have a significant impact on wage. Based on this result, enterprises can optimize the salary structure according to these factors and formulate a more fair and competitive salary plan.
Keywords
Analysis of Variance; Salary Level; Enterprise Nature; Enterprise Scale; Experience Interval; Academic Requirements
References
[1]Yang Fan. Analysis on the Construction of Enterprise Human Resource Management Information System. China Market, 2025, (01): 102-105. DOI: 10.13939 / j. cnki. zgsc. 2025.01.025.
[2]He Ouxiang. Enterprise human resources salary management problems and countermeasures. China management informatization, 2024,27 (18): 158-160.
[3]Zhang Liuqing. Research on the effectiveness of enterprise salary inchworm effect. Southwest University of Finance and Economics, 2021. DOI: 10.27412 / d. cnki. gxncu.2021.001423.
[4]Liu Fang, Dong Fenyi. Research on the diagnosis and treatment of multicollinearity in econometrics. Journal of Zhongyuan University of Technology, 2020, 31 (01): 44-48 + 55.
[5]Liang Ying, Zhang Yuhai. Correct application and expression of multiple linear regression method. Chinese Journal of Child Health, 2020, 28 (02): 230-232.
[6]Wang Zhuo, Zeng Pingtao, Yao Xiangxiu. Research on the demand characteristics and salary influencing factors of data research and development posts. Modern Marketing (Late Edition), 2023, (08): 124-126. DOI: 10.19932 / j. cnki.22-1256 / F.2023.08.124.
[7]Yu Wanlu, Zhang Sijie. Analysis of the influencing factors of data analysis post salary under the penalty GLM algorithm. Industry and Technology Forum, 2023, 22 (17): 49-51.
[8]Yu Yue, Zhang Ge, Yue Xiaojing, et al. Research on human salary management and performance optimization system based on machine learning. Information technology, 2024, (10): 111-119.DOI: 10.13274 / j. cnki. hdzj.2024.10.017.
[9]Lin Wei, Han Wenhui. Analysis of the application of salary management in enterprise human resource management. China Electronic Business, 2025, (01): 70-72.