Predict the sales of New-energy Vehicle using linear regression analysis

Abstract

New-energy Vehicle is a new form of vehicle that uses environmental friendly energy and becomes a popularity in recent years. It is important for both the consumers and the producers to search for factors that might affect the sales of New-energy Vehicle. In this study, three linear regression models are examined to determine factors that have significant effect on sales of New-energy Vehicle. ANOVA analysis is conducted to test the model validity and to compare the effect of the three models obtained. Result shows that disposable income, number of charging station, subsidies and literacy level positively affect the sales of New-energy Vehicle.

Harnessing Regression Analysis and Machine Learning to Unveil the Market Dynamics and Environmental Impact of New Energy Vehicles

“…This condition, characterized by high correlations between independent variables, frequently occurs in complex datasets typical of global markets [4] . Ridge regression, notable for its regularization parameter λ, effectively reduces the coefficients of less significant predictors, thereby mitigating the risk of overfitting and enhancing the model's predictive accuracy [5][6] . The dataset compiled for this analysis was extensive, incorporating variables such as electric vehicle (EV) sales, market shares, gross domestic product (GDP), the density of charging infrastructure, and governmental incentives across various countries [7][8] .…”