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This is a Quantitative Study study.

September 12, 2025

Application of linear regression model in forecasting corporate income tax revenue: A case study of Tax sub-department Region I

Key Points

The linear regression model significantly correlates with corporate income tax obligations based on financial performance metrics.
Key variables like cost of goods sold and liquidity ratio explain variations in return on sales among 213 enterprises.
The study utilized firm-level data from Regional Tax Office I during the 2023-2024 period to validate the model's effectiveness.
Findings highlight the importance of financial performance indicators in predicting corporate income tax revenue.

Abstract

This study analyzes the applicability of the multiple linear regression model in forecasting corporate income tax revenues based on firm-level financial performance indicators. Utilizing data from 213 enterprises under the jurisdiction of Regional Tax Office I during the 2023-2024 period, the research examines the influence of variables such as cost of goods sold (COGS), liquidity ratio, and asset utilization efficiency on return on sales (ROS), which is used as a proxy for financial performance. The regression results indicate that these variables significantly explain the variation in ROS, thereby reflecting a correlation with corporate income tax obligations.

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