Relationship Between Product Sales and Advertising Investment Based on Linear Regression Analysis and Discussion on the Best Advertising Strategy

Zhongyu Zhang

doi:10.54254/2754-1169/2024.19756

Research Article

Open access

Published on 6 January 2025

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Zhang,Z. (2025). Relationship Between Product Sales and Advertising Investment Based on Linear Regression Analysis and Discussion on the Best Advertising Strategy. Advances in Economics, Management and Political Sciences,154,173-178.

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Relationship Between Product Sales and Advertising Investment Based on Linear Regression Analysis and Discussion on the Best Advertising Strategy

Zhongyu Zhang *^,1,

¹ Tianjin Farragut School

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2754-1169/2024.19756

Abstract

In today's competitive business landscape, companies seek to optimize marketing strategies, with advertising investment significantly impacting product sales. However, identifying the most effective advertising plan is complex. This paper examines the relationship between product sales and advertising investment using linear regression analysis. By collecting data and establishing a linear regression model, it aims to identify the optimal advertising strategy for maximizing product sales. This paper mainly discusses the relationship between product sales and advertising investment by using linear regression analysis method. Through the data collection, the linear regression model establishment and analysis, it aims to determine the best advertising scheme to achieve the maximum sales of products. The research thoroughly details the principles of linear regression analysis, covering data collection, model development, and validation. It discusses advertising strategies based on model results, using case studies for analysis. The text presents relevant data and variables, with visuals to demonstrate practical applications of linear regression equations and their derivations, laying a foundation for enterprise advertising recommendations.

Keywords

Linear regression analysis, Product sales, Advertising investment, Advertising strategy, Electronic enterprise

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References

[1]. Freedman, D. A. (2009). Statistical models: Theory and practice (p. 23). Cambridge University Press.

[2]. Nachtsheim, C. J., & Neter, J. (2010). Applied linear regression models (pp. 12, 29). McGraw-Hill/Irwin.

[3]. Box, G. E. P., & Draper, N. R. (1987). Empirical model-building and response surfaces (pp. 35-40). Wiley.

[4]. Johnson, R. A., & Wichern, D. W. (2007). Applied multivariate statistical analysis (pp. 89-121). Pearson Education.

[5]. Myers, R. H. (1990). Classical and modern regression with applications (pp. 8-11). Duxbury Press.

[6]. Montgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to linear regression analysis (pp. 88-90). Wiley.

[7]. Mendenhall, W., Beaver, R. J., & Beaver, B. M. (2012). Introduction to probability and statistics (pp. 55-56). Cengage Learning.

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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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About volume

Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis

Conference website: https://2024.icftba.org/

ISBN：978-1-83558-865-9(Print) / 978-1-83558-866-6(Online)

Conference date: 4 December 2024

Editor：Ursula Faura-Martínez

Series: Advances in Economics, Management and Political Sciences

Volume number: Vol.154

ISSN：2754-1169(Print) / 2754-1177(Online)

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