Residual diagnostics is the process of analyzing the residuals, which are the differences between the observed values and the values predicted by a statistical model. This analysis is crucial for assessing how well the model fits the data and whether any assumptions underlying the model have been violated. By examining residuals, researchers can identify potential issues such as non-linearity, heteroscedasticity, and model specification errors that could affect the validity of their results.
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