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Research on the Revenue of Basketball Celebrity Endorsement Brands — Based on Regression Model
- 1 Quanzhou Normal University, Quan Zhou, China, 362018
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Abstract
Nowadays, the influence of sports brands is increasing, and many sports brands choose to sign well-known sports stars to enhance their brand influence. Chinese sports brands are also like this, increasing brand awareness by signing a large number of star players, and many of these brands choose to sign NBA stars as spokespersons. But are the profits brought by different levels of basketball stars different? This article will analyze and discuss this. This article uses regression models to analyze the basketball stars at different levels, in order to determine which category of basketball stars can bring the greatest income to the brand. Firstly, this article collected sales data for five players' respective brands: Aaron Gordon, Nikola Jokic, Klay Thompson, Keldon Johnson, and Austin Reaves. And preprocess the five sets of data using Visual Studio Code, integrate the five sets of data and export them to Excel. Then, the data was imported using R studio, analyzed using R language, and four regression models were created. The next step is to determine whether different factors bring good benefits through the significance of their respective factors in each regression model. Finally, by comparing the five players, it can be inferred which level of star is more suitable for brand endorsement and brings the greatest benefits.
Keywords
Regression, basketball star, R language, data analysis
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Cite this article
Li,Y. (2024). Research on the Revenue of Basketball Celebrity Endorsement Brands — Based on Regression Model. Advances in Economics, Management and Political Sciences,131,137-143.
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 8th International Conference on Economic Management and Green Development
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