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Deviation (statistics)

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Title: Deviation (statistics)  
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Subject: Errors and residuals, Extreme value theory, Periodic matrix set, Mean deviation, Kurtosis
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Deviation (statistics)

In mathematics and statistics, deviation is a measure of difference between the observed value of a variable and some other value, often that variable's mean. The sign of the deviation (positive or negative), reports the direction of that difference (the deviation is positive when the observed value exceeds the reference value). The magnitude of the value indicates the size of the difference.

Contents

  • Types of deviation 1
  • Unsigned or absolute deviation 2
  • Measures of deviation 3
    • Central tendency 3.1
    • Dispersion 3.2
    • Normalization 3.3
  • See also 4

Types of deviation

A deviation that is a difference between an observed value and the true value of a quantity of interest (such as a population mean) is an error and a deviation that is the difference between the observed value and an estimate of the true value (such an estimate may be a sample mean) is a residual. These concepts are applicable for data at the interval and ratio levels of measurement.

Unsigned or absolute deviation

In statistics, the absolute deviation of an element of a data set is the absolute difference between that element and a given point. Typically the deviation is reckoned from the central value, being construed as some type of average, most often the median or sometimes the mean of the data set.

D_i = |x_i-m(X)|

where

Di is the absolute deviation,
xi is the data element
and m(X) is the chosen measure of central tendency of the data set—sometimes the mean (\overline{x}), but most often the median.

Measures of deviation

Central tendency

For an unbiased estimate, the sum of the signed deviations across the entire set of all observations from the overall sample mean is always zero, and the expected, mean, or average deviation is also zero; conversely, a nonzero average deviation quantifies the bias exhibited by the estimate.

Dispersion

Statistics of the distribution of deviations are used as measures of statistical dispersion.

Normalization

Deviations have units of the measurement scale (for instance, meters if measuring lengths). One can nondimensionalize in two ways.

One way is by dividing by a measure of scale (statistical dispersion), most often either the population standard deviation, in standardizing, or the sample standard deviation, in studentizing (e.g., Studentized residual).

One can scale instead by location, not dispersion: the formula for a percent deviation is the observed value minus accepted value divided by the accepted value multiplied by 100%.

See also


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