Residuals in Statistics

Residuals in statistics are the differences between observed values and the values predicted by a model, such as a line of best fit. Essentially, if you have data points and a model that tries to explain or predict these points, each residual shows how far off the prediction was from the actual data. Small residuals mean the model’s predictions are close to reality, while large residuals indicate less accurate predictions. Residual analysis helps assess the model's effectiveness and identify patterns or errors that may suggest improvements.