Application of Linear Regression Analysis in Predicting the Index of An Electronic Store

Authors

  • Chunyue Lu

DOI:

https://doi.org/10.54097/yjxd2y05

Keywords:

Linear regression analysis; ROI; model prediction.

Abstract

This paper illustrates how linear regression analysis can be used to establish a suitable mathematical model for the return on investment (ROI) of the store from June to December in 2020. What’s more, preliminary analysis of the known data will be explained. Then, the ROI of the store from January to May is estimated by using the model, and the limitations and uncertainties of using the analytical method will be stated by reflecting the process of the analysis. The paper aims to prove the feasibility and rationality of using the method of linear regression analysis to build a model for the prediction of future figures and show exactly how linear regression analysis can be exactly applied in real-world cases. Also, the result shows that the method is effective and reasonable, while there are still errors and uncertainties of the estimation, which makes merchants must consider about all factors when making decisions.

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References

Anastasiia Kameneva. Development of E-commerce in China and in Russia. East China Normal University, 2018, 1-74.

Liu Hao, Li Yunze, Cao Qinyu, Qiu Guang, Chen Jiming. Estimating Individual Advertising Effect in E-Commerce. College of Control Science and Engineering, Zhejiang University, 2019, 1-7.

Liu Meili. Research on ROI of product development and operation. Inner Mongol University of Technology, 2007, 1-63.

William Murdoch, Stephen Polasky, Kerrie A. Wilson, Hugh P. Possingham, Peter Kareiva, Rebecca Shaw. Maximizing return on investment in conservation. Biological Conservation, 2007, 375-388.

Wang Weijia. Research on the improvement of return on investment of Company B. Dalian University of Technology, 2020, 1-43.

Li Xiaoyu. Application of linear regression analysis in predicting the index of an electronic store. Chinese Market, 2022, 185-187.

Zhao Yanjun, Pan Hongxia, Ma Chunmao, Liu Yongjiang. Failure Rate Prediction Based on Grey Linear Regression Combined Model. China Academic Journal Electronic Publishing House, 2014, 34(4): 664-667.

Matthew Avialdo Pratama, Wowon Priatna. Utilizing linear regression for predicting sales of top-performing products. Internal Journal of Information Technology and Computer Science Applications, 2023, 1(3): 174-180.

Qiu Tianqi. Predicting the Federal Funds Rate: A Linear Regression Analysis. School of Arts & Sciences, University of Rochester, 2023, 57-62.

J. Watada et al. Reconstruction of Flow Rate History Using Linear Regression. Institute of Hydrocarbon Recovery and Petroleum Engineering Department, University Technology Petronas, Malaysia, the authors, 2023, 22-35.

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Published

29-03-2024

How to Cite

Lu, C. (2024). Application of Linear Regression Analysis in Predicting the Index of An Electronic Store. Highlights in Science, Engineering and Technology, 88, 1186-1191. https://doi.org/10.54097/yjxd2y05