Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

August 5, 2024


Pages


DOI

Article

Multiple regression analysis of the mechanism of the role of infrastructure development in rural economic growth

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Authors

Mingda Zhu Affiliation:
School of Economics and Management, Wuhan University, Wuhan, Hubei, 430072, China.


Abstract

The improvement of China’s economic level makes the society’s requirements and standards for infrastructure constantly improve. In order to effectively promote the rapid development of the rural economy, it is necessary to strengthen the construction of rural infrastructure. This paper provides a comprehensive plan for rural infrastructure construction and analyzes its mechanism of action on rural economic development in depth. Taking rural economic growth as the explanatory variable and infrastructure construction as the explanatory variable, the multiple linear regression model is chosen to analyze the impact of rural infrastructure on rural economic growth. The unknown parameters are estimated by the least squares method. The model is tested and modified based on the diagnostic methods of covariance expansion factor and other covariates to obtain the final results. Through empirical analysis, rural economic growth = -15.1935 + 0.184*rural transportation + 0.0983*rural education + 2.4923*agricultural science and technology + 0.3652*agricultural water conservancy, and agricultural science and technology has the greatest impact on rural economic growth. The local area can improve rural infrastructure in three aspects: investment strength, investment focus, and investment and financing mechanisms.


Keywords

Multiple regression analysis, Least squares method, Multiple covariance test, Rural economy, Infrastructure development, 03B70


Citation

Zhu, M. (2024). Multiple regression analysis of the mechanism of the role of infrastructure development in rural economic growth. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-2223
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23 References
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