Understanding Residuals

Residuals represent the difference between actual observed data and a model's predictions, highlighting the model's shortcomings. When patterns emerge in the residuals, it indicates that the model has overlooked certain factors. Ideally, if a model captures all predictable elements, the residuals should appear as random noise, signaling a well-fitted model.