Search results
Results from the WOW.Com Content Network
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 ...
This is an important technique in the detection of outliers. ... line going through (0, 0) to the points (1, 4), (2, − ... Cook's distance – a measure of changes ...
Therefore, the authors suggest investigating those points with DFFITS greater than . 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]
A frequent cause of outliers is a mixture of two ... the Student t distribution with n-2 degrees of ... such as Cook's distance. [30] If a data point ...
For an approximately normal data set, the values within one standard deviation of the mean account for about 68% of the set; while within two standard deviations account for about 95%; and within three standard deviations account for about 99.7%. Shown percentages are rounded theoretical probabilities intended only to approximate the empirical ...
An outlier may be defined as a data point that differs markedly from other observations. [6] [7] A high-leverage point are observations made at extreme values of independent variables. [8] Both types of atypical observations will force the regression line to be close to the point. [2]
Pages in category "Statistical outliers" The following 17 pages are in this category, out of 17 total. ... Cook's distance; D. Dixon's Q test; Dragon king theory; G ...
High-leverage points, if any, are outliers with respect to the independent variables. That is, high-leverage points have no neighboring points in R p {\displaystyle \mathbb {R} ^{p}} space, where p {\displaystyle {p}} is the number of independent variables in a regression model.