Regression-based modeling builds reliable predictive and explanatory models systematically.

Model Specification defines dependent and independent variables with appropriate functional form.

Data Preparation handles missing values, transforms variables, and splits data.

Diagnostic Testing validates assumptions through residual analysis and multicollinearity checks.

Validation confirms performance on held-out data before deployment.