Quantitative Research on New Energy Vehicle Development Based on Multiple Linear Regression

Abstract

Analyzing the factors influencing the development of China's new energy vehicles is beneficial for understanding the progress of this sector and boosting its growth. This study utilizes gray correlation analysis and a multiple linear regression model to examine the relationship between diverse factors and the progress of new energy vehicles. To identify the primary determinants of new energy vehicle sales, data on pertinent indicators were collected. Initially, seven evaluation indicators affecting the sales of new energy vehicles were identified by reading many literatures. Subsequently, gray correlation analysis was employed to calculate the degree of association between these indicators and new energy vehicles. It was discovered that the gray correlation between the number of public charging piles and the sales of new energy vehicles is 0.922, and the corresponding gray correlations of the remaining six indicators are all 0.7-0.8, which means that the correlation between the indicators and the sales of new energy vehicles is obvious, and all of them are used to establish the multiple linear regression model. The results show that the number of public charging piles has the most significant effect, with a regression coefficient of , which indicates that strengthening the charging infrastructure can effectively promote the growth of its sales.