International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The analysis of count data has become increasingly important in financial and managerial research, particularly when information is collected in the form of frequencies or event counts. Conventional linear regression models are often unsuitable for such data due to their discrete and non-negative nature. Poisson regression provides an effective alternative by modelling count data within a probabilistic framework. This study applies Poisson regression analysis using statistical and data science tools to examine financial statement related count data collected from Konigtronics Private Limited. Primary data were obtained through a structured questionnaire survey from 63 respondents and supported by secondary data from company records. Since the responses represent frequency-based observations, Poisson regression was employed and parameters were estimated using maximum likelihood estimation. Descriptive statistics and correlation analysis were also used to support the findings. The results indicate that identifying relationships among financial variables, particularly cash flow patterns and reporting practices, improves the quality and reliability of financial statements. The study concludes that Poisson regression is a suitable and effective tool for analysing count data and supporting informed financial decision making.

Keywords: Poisson Regression, count data analysis, financial statements, maximum likelihood estimation, cash flow analysis, financial reporting, statistical modelling, managerial decision making.

Downloads: | DOI: 10.17148/IJARCCE.2025.1412133