Authors : Madhuparna Das Hait; Priya Das; Washim Akram; Siddhartha Chatterjee

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/3j52ekae

DOI : https://doi.org/10.38124/ijisrt/25jul349

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Abstract : The main objective of this research is to determine which of the three methods of regression; Ordinary Least Squares (OLS) regression, Baseline regression, and Polynomial regression offers the most accurate predictive capability and an ability to capture the associations between two variables. Other assessment indicators include R squared and Mean squared error (MSE) while graphical techniques include residual charts. The paper presents a concise review of the linear regression method, the mathematical background of the method, and the procedure for improving the efficiency of the model by selecting relevant features. It discusses the use OLS regression as the fundamental technique of statistical inference and its relative accuracy to other methods. Using regression lines, residual graphs, outliers influence and effects of outliers, the research shows how reliable predictions can be made using such models. This work contributes to the understanding of statistical modelling, giving practicable guidelines for enhancing data analysis techniques for all fields of study, mainly economics, natural science, and social science to enable improved decision-making and enhanced accuracy of the analysis.

Keywords : Predictive Accuracy, Ordinary Least Squares (OLS), Model Evaluation Metrics.

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