The article discusses multiple regressions, which aim to explore relationships between many factors and a result. Unlike simple regressions that focus on one factor, these regressions investigate how several factors together impact an outcome. Using methods built on simple regression principles, such as Ordinary Least Squares and Gini regression estimators, researchers can find relationships without assuming the outcome follows a straight line. By analyzing the connections between different factors and the outcome, they gain a deeper understanding of how these pieces fit together. This approach allows for more robust and flexible analysis of complex relationships in data.