title: New application of multiple linear regression method - A case in China air quality
metadata.name: dc.contributor.author
metadata.value: He, Yang
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Qi, Dongfang
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Bure, Vladimir M.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2023-03-14T13:40:50Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2023-03-14T13:40:50Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2022-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: He, Y., Qi, D., & Bure, V. M. (2023). New application of multiple linear regression method - A case in China air quality. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 18(4), 516-526.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu10.2022.406
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/39309
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: 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 PM2.5, followed by O3.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 18; Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: multiple linear regression
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: air pollution
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: AQI
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: hypothesis test
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: PM2.5
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: O3
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: New application of multiple linear regression method - A case in China air quality
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 15527-Текст статьи-52064-1-10-20230308.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/39309/1/15527-%d0%a2%d0%b5%d0%ba%d1%81%d1%82%20%d1%81%d1%82%d0%b0%d1%82%d1%8c%d0%b8-52064-1-10-20230308.pdf
resources.format: Adobe PDF
resources.size: 859,26 kB