Residuals Analysis

Residuals analysis is a method used in statistics to evaluate how well a model, like a regression, fits the data. Residuals are the differences between the actual observed values and the values predicted by the model. By analyzing these differences, we can identify patterns or inconsistencies that suggest the model may not be accurately capturing the data’s relationship. This helps statisticians assess the reliability of the model, check for violations of assumptions, and improve its accuracy. Overall, residuals analysis is a key step in ensuring a statistical model provides a meaningful and trustworthy representation of the data.