Cook's Distance is a measure used to identify influential observations in a dataset. Specifically, it quantifies the change in the regression coefficients when a particular observation is removed from the analysis. High values of Cook's Distance indicate that the observation has a substantial impact on the model's estimates, potentially skewing the results.
