Model diagnostics refers to the set of techniques used to evaluate the performance of a statistical model, particularly in identifying any issues with the fit or assumptions made by the model. This process helps ensure that the model adequately captures the underlying relationship in the data, allowing for valid inference and predictions. Through various diagnostic tools, one can assess residuals, check for multicollinearity, and validate assumptions like linearity and homoscedasticity.
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