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In statistics, Cook's distance or Cook's D is a commonly used estimate of the influence of a data point when performing a least-squares regression analysis. [1] In a practical ordinary least squares analysis, Cook's distance can be used in several ways: to indicate influential data points that are particularly worth checking for validity; or to indicate regions of the design space where it ...
Although the raw values resulting from the equations are different, Cook's distance and DFFITS are conceptually identical and there is a closed-form formula to convert one value to the other. [ 3 ] Development
Ralph Dennis Cook (born June 20, 1944) is an American statistician, mostly known for Cook's distance [1] and the Cook–Weisberg test. [2] Cook is a professor of statistics at the University of Minnesota. After graduating from Northern Montana College (1967), Cook earned his master's (1969) and Ph.D. (1971) degrees from Kansas State University. [3]
Specifically, for some matrix , the squared Mahalanobis distance of (where is row of ) from the vector of mean ^ = = of length , is () = (^) (^), where = is the estimated covariance matrix of 's. This is related to the leverage h i i {\displaystyle h_{ii}} of the hat matrix of X {\displaystyle \mathbf {X} } after appending a column vector of 1 ...
Various methods have been proposed for measuring influence. [3] [4] Assume an estimated regression = +, where is an n×1 column vector for the response variable, is the n×k design matrix of explanatory variables (including a constant), is the n×1 residual vector, and is a k×1 vector of estimates of some population parameter .
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This page was last edited on 23 December 2022, at 18:54 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
Working in batches if necessary, add the thighs in a single layer and cook on one side until browned, 4 to 6 minutes. Flip and let cook for another 1 to 2 minutes on the other side, then transfer ...