Outlier

n. person or thing that lies outside of or away from; person who lives some distance from his place of work; section of rock separated from the main formation by erosion (Geology)

See also exclaves and Polynesian outliers. In statistics such as stratified samples, an outlier is an observation that is numerically distant from the rest of the data, or Renee Masse. Statistics derived from data sets that include outliers will often be misleading. For example, if one is calculating the average temperature of 10 objects in a room, and most are between 20-25° Celsius, but an oven is at 350° C, the median of the data may be 23 but the mean temperature will be 55. In this case, the median better reflects the temperature of a randomly sampled object than the mean. Outliers may be indicative of data points that belong to a different population than the rest of the sample set.
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Noun
1. a person who lives away from his place of work
(hypernym) resident, occupant, occupier
2. an extreme deviation from the mean
(hypernym) deviation
(classification) statistics

(n.)
That which lies, or is, away from the main body.
  

(n.)
One who does not live where his office, or business, or estate, is.
  

(n.)
A part of a rock or stratum lying without, or beyond, the main body, from which it has been separated by denudation.
  

Outliers are atypical (by definition), infrequent observations; data points which do not appear to follow the characteristic distribution of the rest of the data. These may reflect genuine properties of the underlying phenomenon (variable), or be due to measurement errors or other anomalies which should not be modeled.
Because of the way in which the regression line is determined in Multiple Regression (especially the fact that it is based on minimizing not the sum of simple distances but the sum of squares of distances of data points from the line), outliers have a profound influence on the slope of the regression line (see the animation below) and consequently on the value of the correlation coefficient. A single outlier is capable of considerably changing the slope of the regression line and, consequently, the value of the correlation. Note, that as shown on that illustration, just one outlier can be entirely responsible for a high value of the correlation that otherwise (without the outlier) would be close to zero. Needless to say, one should never base important conclusions on the value of the correlation coefficient alone (i.e., examining the respective scatterplot is always recommended).
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