Mathematically, Cook's Distance for an observation \(i\) is calculated as: \[ D_i = \frac{(e_i^2 / p) \cdot (h_i / (1 - h_i)^2)}{MSE} \] where: - \(e_i\) is the residual for observation \(i\). - \(p\) is the number of predictors in the model. - \(h_i\) is the leverage of observation \(i\). - \(MSE\) is the mean squared error of the model.
