The residualization procedure has been applied in many different fields to estimate models with multicollinearity. However, there exists a lack of understanding of this methodology and some authors discourage its use. This paper aims to contribute to a better understanding of the residualization procedure to promote an adequate application and interpretation of it among statistics and data sciences. We highlight its interesting potential application, not only to mitigate multicollinearity but also when the study is oriented to the analysis of the isolated effect of independent variables. The relation between the residualization methodology and the Frisch-Waugh-Lovell (FWL) theorem is also analyzed, concluding that, although both provide the same estimations, the interpretation of the estimated coefficients is different. These different interpretations justify the application of the residualization methodology regardless of the FWL theorem. A real data example is presented for a better illustration of the contribution of this paper.

翻译：残差化程序已被应用于许多不同领域以估计存在多重共线性的模型。然而，目前对这一方法论的理解尚不充分，且部分学者不鼓励其使用。本文旨在促进对残差化程序的深入理解，以推动其在统计学与数据科学领域的恰当应用与合理解释。我们重点阐述了其潜在的有趣应用——不仅可用于缓解多重共线性问题，亦适用于以分析自变量独立效应为导向的研究。本文同时分析了残差化方法与Frisch-Waugh-Lovell（FWL）定理的关联，结论表明：尽管两者能提供相同的估计结果，但对估计系数的解释存在差异。这种解释差异证明了残差化方法独立于FWL定理的应用价值。文中通过实际数据案例进一步阐释了本研究的贡献。