Author
Listed:
- Shukun Chen
(School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China
New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong, Jinan 250103, China
Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong, Jinan 250103, China)
- Yufu Ning
(School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China
New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong, Jinan 250103, China
Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong, Jinan 250103, China)
- Lihui Wang
(School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China
New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong, Jinan 250103, China
Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong, Jinan 250103, China)
- Shuai Wang
(School of Information Engineering, Shandong Youth University of Political Science, Jinan 250103, China
New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong, Jinan 250103, China
Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong, Jinan 250103, China)
Abstract
According to the analysis of historical tourism data, it was found that tourism revenue is influenced by multiple factors, and there exists a linear relationship between these factors and tourism revenue. Therefore, this paper employs a linear regression model to investigate the factors influencing tourism revenue. However, research on tourism data has found that the disturbance term of the linear regression model is not frequency-stable. This indicates that the disturbance term should be an uncertain variable rather than a random variable. Therefore, this paper adopts an uncertain linear regression analysis model and employs the tourism data of Shandong Province in China from 2011 to 2020 as the sample to investigate the factors influencing tourism revenue. The study provides parameter estimation and residual analysis of the model, as well as predictions and confidence intervals of tourism revenue. Additionally, through an uncertain hypothesis test, it was verified that the adopted model fitted the relevant tourism data well. The results show that factors such as the number of travel agencies, railway length, domestic tourist numbers, and per capita disposable income of urban residents have a significant impact on tourism revenue. Based on the study, recommendations and measures for improving tourism revenue of Shandong Province are proposed.
Suggested Citation
Shukun Chen & Yufu Ning & Lihui Wang & Shuai Wang, 2023.
"Research on the Factors Influencing Tourism Revenue of Shandong Province in China Based on Uncertain Regression Analysis,"
Mathematics, MDPI, vol. 11(21), pages 1-12, October.
Handle:
RePEc:gam:jmathe:v:11:y:2023:i:21:p:4490-:d:1270790
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