TY - JOUR

T1 - New application of multiple linear regression method - A case in China air quality

AU - He, Yang

AU - Qi, Dongfang

AU - Bure, Vladimir M.

PY - 2022/12

Y1 - 2022/12

N2 - In this paper, we propose an econometric model based on the multiple linear regression method. This research aims to evaluate the most important factors of the dependent variable. To be more specific, we consider the properties of this model, model quality, parameters test, checking the residual of the model. Then, to ensure that the prediction model is optimal, we use the backward elimination stepwise regression method to get the final model. At the same time, we also need to check the properties in each step. Finally, the results are illustrated by a real case in China air quality. The achieved model was applied to predict the 31 capital cities in Сhina's air quality index (AQI) during 2013-2019 per year. All calculations and tests were achieved by using R-studio. The dependent variable is the China's AQI. The control variables are six pollutant factors and four meteorological factors. In summary, the model shows that the most significant influencing factor of the AQI in China is PM_2.5, followed by O_3.

AB - In this paper, we propose an econometric model based on the multiple linear regression method. This research aims to evaluate the most important factors of the dependent variable. To be more specific, we consider the properties of this model, model quality, parameters test, checking the residual of the model. Then, to ensure that the prediction model is optimal, we use the backward elimination stepwise regression method to get the final model. At the same time, we also need to check the properties in each step. Finally, the results are illustrated by a real case in China air quality. The achieved model was applied to predict the 31 capital cities in Сhina's air quality index (AQI) during 2013-2019 per year. All calculations and tests were achieved by using R-studio. The dependent variable is the China's AQI. The control variables are six pollutant factors and four meteorological factors. In summary, the model shows that the most significant influencing factor of the AQI in China is PM_2.5, followed by O_3.

KW - МНОЖЕСТВЕННАЯ ЛИНЕЙНАЯ РЕГРЕССИЯ

KW - ЗАГРЯЗНЕНИЕ ВОЗДУХА

KW - AQI

KW - проверка гипотез

KW - PM_2.5

KW - O-3

KW - MULTIPLE LINEAR REGRESSION

KW - air pollution

KW - HYPOTHESIS TEST

UR - https://elibrary.ru/item.asp?id=50530364

UR - https://applmathjournal.spbu.ru/article/view/15527

U2 - 10.21638/11701/spbu10.2022.406

DO - 10.21638/11701/spbu10.2022.406

M3 - Article

VL - 18

SP - 516

EP - 526

JO - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

JF - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

SN - 1811-9905

IS - 4

ER -