A comparative evaluation of ols, robust regression, and quantile regression under distributional heterogeneity and outlier contamination
This study examines the impact of key socio-economic factors—education, work experience, gender, and age—on income, with a particular focus on capturing heterogeneity across the income distribution. A structured preprocessing framework is employed, including missing value treatment, categorical variable transformation, and outlier detection using the Interquartile Range (IQR) method. The dataset is divided into training (80%) and test (20%) subsets to assess model generalizability, and K-fold cross-validation is applied to evaluate predictive stability.