The article explores different methods used in regression analysis to understand the relationship between variables. Parametric regression assumes a linear relationship, while non-parametric methods are used when this assumption doesn't hold. However, non-parametric methods can be challenging to interpret with multiple variables. Semiparametric regression combines both approaches to model complex relationships. The study shows that semiparametric regression can be useful when assumptions of parametric regression are not met. Overall, the article discusses parametric, non-parametric, and semiparametric regression methods and their applications in analyzing relationships between variables.