Research on Insurance Cost Prediction Model Based on Linear Regression

Authors

  • Xinyang Liu
  • Siyu Wang
  • Xu Fang
  • Senyao Zhang
  • Bohan Lin

DOI:

https://doi.org/10.54097/p0xvz169

Keywords:

Extreme Weather, Least Square Method, Prediction Model, Correlation Coefficient.

Abstract

The insurance industry is experiencing a crisis due to the frequent occurrence of extreme weather worldwide, resulting in significant losses across all sectors. This paper aims to mitigate the impact of extreme weather on the insurance industry by using Harbin and Haikou as case studies. The study employs linear regression (least square method) and Pearson correlation coefficient analysis to simulate the insurance cost of the two cities over the next ten years. The results offer insights into the potential profits and losses of insurance costs during different periods. Additionally, this section presents the management mode of urban insurance companies, taking into account local conditions. The model method responds to national policies, but further data is required to support in-depth research and improvement.

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References

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Published

30-06-2024

How to Cite

Liu, X., Wang, S., Fang, X., Zhang, S., & Lin, B. (2024). Research on Insurance Cost Prediction Model Based on Linear Regression. Highlights in Science, Engineering and Technology, 105, 222-229. https://doi.org/10.54097/p0xvz169