Description: Overlapping variables are those that share common information or are correlated with each other. In data analysis, these variables can influence the outcomes of a study or experiment, as their interrelationship can provide a deeper understanding of the phenomena being analyzed. Identifying overlapping variables is crucial to avoid multicollinearity, a problem that can distort the results of statistical models, such as regression analysis. These variables can be both qualitative and quantitative, and their analysis allows researchers to discern patterns and relationships that might otherwise go unnoticed. Furthermore, the study of overlapping variables is fundamental in creating predictive models, where the goal is to maximize prediction accuracy by considering the interactions among different factors. In summary, overlapping variables are an essential component in data analysis, as their proper identification and management can significantly enhance the quality of the results obtained.
