<![CDATA[Multiple linear regression is a central analytical tool in econometric research used to model the relationship between a dependent variable and multiple independent variables. However, the accuracy and validity of such models are highly dependent on classical assumptions, particularly multicollinearity and linearity. Multicollinearity, characterized by high correlations among predictor variables, can inflate standard errors and obscure the true effects of individual variables. Linearity, meanwhile, ensures that the relationships between variables follow a straight-line pattern, which is essential for valid estimation and inference. This theoretical study aims to deepen the understanding of both assumptions, explore their causes, impacts, and identify methodological approaches for detection and correction. Employing a descriptive literature review method, the study synthesizes insights from contemporary econometric research to provide a conceptual framework for handling these issues. Key findings highlight that multicollinearity often arises from overlapping variables, small samples, and measurement errors, and can be addressed through variable elimination, transformation, or penalized regression techniques such as ridge and lasso regression. Linearity violations, frequently resulting from model misspecification or temporal dependencies, may be mitigated using data transformations, polynomial regression, or robust regression approaches. The study concludes that proper diagnostic tools and corrective strategies are essential for improving model reliability and enhancing the credibility of econometric findings in economic research.]]>
