Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022)

Economic Statistics Issues under Big Data——Taking China as an Example

Authors
Yining Yang1, *
1Henan Experimental High school, Henan province, Zhengzhou, 450002, China
*Corresponding author. Email: Yiningyang02@163.com
Corresponding Author
Yining Yang
Available Online 29 December 2022.
DOI
10.2991/978-94-6463-056-5_8How to use a DOI?
Keywords
house prices; population; multicollinearity; regression; population structure
Abstract

China’s economy has increased dramatically over the past decades. Along with the development of economy, the house prices also increased sharply. Thus, it is necessary to investigate factors contributing to the high house prices. Existing literature demonstrates that GDP, salary, total retail sales of consumer goods and population strongly correlated with increasing house prices. However, there is a FU study that investigated the relationship between house prices and those factors in China. We contacted the study to fill the gap.

This study used a quantitative method to analyze the relationships. Specifically, descriptive statistics and regression models Boo request other house prices. The data was selected from 10 large cities in China from 2011 to 2019. This paper finds that there is a strong relationship between house prices and GDP, house prices and total retail sales of consumer goods and house prices and average yearly salary. However, contrasting with past research, the result shows a negative relationship between house prices and the total number of populations. The limitation of the study includes small sample size end problems of multicollinearity. Further research can be conducted using other factors such as fertility rate as the explanatory variable.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022)
Series
Atlantis Highlights in Computer Sciences
Publication Date
29 December 2022
ISBN
10.2991/978-94-6463-056-5_8
ISSN
2589-4900
DOI
10.2991/978-94-6463-056-5_8How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Yining Yang
PY  - 2022
DA  - 2022/12/29
TI  - Economic Statistics Issues under Big Data——Taking China as an Example
BT  - Proceedings of the 2022 2nd International Conference on Management Science and Software Engineering (ICMSSE 2022)
PB  - Atlantis Press
SP  - 40
EP  - 52
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-056-5_8
DO  - 10.2991/978-94-6463-056-5_8
ID  - Yang2022
ER  -