residual analysis

Residual analysis is a method used to evaluate the accuracy of a predictive model, like a regression model. It involves examining the residuals, which are the differences between the actual outcomes and the values predicted by the model. By analyzing these differences, we can determine if the model is performing well or if there are patterns indicating potential issues. Ideally, residuals should be randomly scattered around zero, suggesting the model captures the underlying data well. If patterns emerge, it may signal that the model needs improvement or that important factors were overlooked.