<![CDATA[The intercept is a fundamental component in regression modeling that often receives limited attention in data analysis practice. This article presents a comprehensive literature review on the role of the intercept in enhancing the predictive accuracy of regression models. Through an examination of reputable national and international journals, the study identifies that the intercept significantly contributes to (1) the interpretation of model constants, (2) improvement of prediction accuracy, and (3) the validity of parameter estimation. The review reveals that ignoring or omitting the intercept without a strong statistical justification may lead to estimation bias and reduced predictive quality of the model. The practical implications of this study guide researchers in deciding whether to include or exclude the intercept in regression models, particularly in social, economic, and educational research.]]>
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