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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
It's the third equation that's incorrect: the denominator should be p s^2, not (1+p) s^2. Primrose61 18:52, 11 November 2013 (UTC) []. The denumerator in the second equation should be p * s^2, and in the terms of MSE, s^2 = MSE * n / (n - p), where n is the number of observations, and p is the number of parameters, so the whole equation should be:
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If the errors are independent and normally distributed with expected value 0 and variance σ 2, then the probability distribution of the ith externally studentized residual () is a Student's t-distribution with n − m − 1 degrees of freedom, and can range from to +.
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 ...
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