The Data Analysis of Shanghai Air Quality Index Based on Linear Regression Analysis

Abstract

In this paper, the actual data related to air quality in Shanghai is selected, and six factors such as PM2.5, PM10, SO2, NO2, O3 and CO concentration are used as the alternative factors affecting AQI. The Pearson correlation coefficient between these six factors and the air quality index was calculated separately, and the correlation between the variables was analyzed. This paper considers the multicollinearity between the six factors, in order to reduce the influence of multicollinearity on the model, comprehensively consider the elimination of some alternative factors. Then use spss software to analyze in depth, take AQI as the dependent variable, and the other factors as the independent variable to carry out multiple linear regression analysis. Finally, the multiple linear regression analysis equation is obtained and predicted based on this model. The results show that the model is for calculating air. The quality index is more accurate. This model is simpler and more efficient than the original AQI calculation model.