Cook's distance is a regression diagnostic measure that quantifies the influence of each observation on the fitted regression coefficients. It is computed as D_i = (r_i^2 / (p * MSE)) * (h_ii / (1 - h_ii)^2) where r_i is the i-th standardized residual, h_ii is the i-th diagonal element of the hat matrix, p is the number of regression parameters, and MSE is the mean squared error. Large values of D_i indicate observations that, if removed, would substantially change the fitted model. It persists through statistical practice and software implementation. [formal: cook_distantia | substrate: mind | horizon: a moment | explicit: yes | epoch: 0.01]
Accepted ontology entry
cook distance
Cook's distance is a regression diagnostic measure that quantifies the influence of each observation on the fitted regression coefficients. It is computed as D_i = (r_i^2 / (p * MSE)) * (h_ii / (1 - h_ii)^2) where r_i is the i-th standardi…
Definition
Why it is in scope
A human-made statistical measure in regression analysis that quantifies the influence of each observation on the fitted regression coefficients — how much the model changes if a single data point is removed
Names and aliases
- cook distanceen · CANONICAL
Relations from this entry
- cmsdai2d503n23vv3e00xn5baSERVES →
Cook's distance measures how much the regression fit changes when an observation is deleted. Its designed purpose is to serve regression analysis by identifying influential data points.
- cmsesl4wm05uq3vv3rv0svp7mDERIVED_FROM →
Cook's distance formula is computed from regression residuals: D_i = (r_i^2 / (p * MSE)) * (h_ii / (1 - h_ii)^2). The measure is derived from and depends on the residuals to quantify influence.
- cmskmodwa04zunobpymq3i7ifINSTANCE_OF →
Cook's distance IS a specific kind of regression diagnostic — it measures the influence of each observation on the fitted coefficients. A competent speaker would say it is a regression diagnostic for influence detection.
- cmskpjusd056nnobpc3khcmusINSTANCE_OF →
Cook distance IS a diagnostic measure — it measures the influence of each observation on a regression model's fitted values. It is a specific kind of diagnostic tool that quantifies how much the model would change if that observation were removed.
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Record identity
- Created
- Aug 8, 2026, 5:34 PM UTC
- Content hash
- 7cd13e58ec96403342ab66985ed35c37163f9750e04e29fefaa680260b789e0f