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Influence statistics measure the effects of individual data points or groups of data points on a statistical analysis. The effect of individual data points on an analysis can be profound, and so the detection of unusual or aberrant data points is an important part of nearly every analysis. Influence statistics typically focus on a particular aspect of a model fit or data analysis and attempt to quantify how the model changes with respect to that aspect when a particular data point or group of data points is included in the analysis. In the context of linear regression, where the ideas were first popularized in the 1970s, a variety of influence measures have been proposed to assess the impact of particular data points.

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