Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 39, 21 June 2024
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Applications of three distinct regression models in GDP predication
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Abstract
This paper introduces the basic theory and formula of linear regression, multiple linear regression, and nonlinear regression. Linear regression is one of the commonly used analysis methods in statistical analysis, which can predict the trend of model data change to a certain extent. Multiple linear regression involves more variables to predict and analyze the change trend of data, and can predict the change of data more accurately. Nonlinear regression can predict the model of arbitrary relationship between variables, thus obtaining more accurate prediction data. In the selection of regression analysis method, data characteristics and problem background should be considered, and model assumptions and validation should be paid attention to ensure accuracy and reliability. In the applications, the paper discusses the application of simple linear regression to Okun’s law and delves into the complex relationship between multiple variables and gross domestic product (GDP). Finally, it uses nonlinear regression equations to analyze the global inflation rate and the annual data, and proves that there is a nonlinear relationship between the two and a downward trend, which is supported by analyzing the data of Australia and Canada.
Keywords
linear regression, multiple linear regression, nonlinear regression, Okun’s law
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Mathematical Physics and Computational Simulation
- ISBN (Print)
- 978-1-83558-463-7
- ISBN (Online)
- 978-1-83558-464-4
- Published Date
- 21 June 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/39/20240592
- Copyright
- 21 June 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited