Mean Difference (other)
Mean difference may refer to: * Mean absolute difference, a measure of statistical dispersion * Mean signed difference, a measure of central tendency See also *Mean deviation (other) Mean deviation may refer to: Statistics * Mean signed deviation, a measure of central tendency * Mean absolute deviation, a measure of statistical dispersion * Mean squared deviation, another measure of statistical dispersion Other * ''Mean Dev ...

Mean Absolute Difference
The mean absolute difference (univariate) is a Statistical dispersion#Measures of statistical dispersion, measure of statistical dispersion equal to the average absolute difference of two independent values drawn from a probability distribution. A related statistic is the #Relative_mean_absolute_difference, relative mean absolute difference, which is the mean absolute difference divided by the arithmetic mean, and equal to twice the Gini coefficient. The mean absolute difference is also known as the absolute mean difference (not to be confused with the absolute value of the mean signed difference) and the Corrado Gini, Gini mean difference (GMD). The mean absolute difference is sometimes denoted by Δ or as MD. Definition The mean absolute difference is defined as the "average" or "mean", formally the expected value, of the absolute difference of two random variables ''X'' and ''Y'' Independent and identically distributed random variables, independently and identically distribut ...
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Mean Signed Difference
In statistics, the mean signed difference (MSD), also known as mean signed deviation, mean signed error, or mean bias error is a sample statistic that summarizes how well a set of estimates \hat_i match the quantities \theta_i that they are supposed to estimate. It is one of a number of statistics that can be used to assess an estimation procedure, and it would often be used in conjunction with a sample version of the mean square error. For example, suppose a linear regression model has been estimated over a sample of data, and is then used to extrapolate predictions of the dependent variable out of sample after the out-of-sample data points have become available. Then \theta_i would be the ''i''-th out-of-sample value of the dependent variable, and \hat_i would be its predicted value. The mean signed deviation is the average value of \hat_i-\theta_i. Definition The mean signed difference is derived from a set of ''n'' pairs, ( \hat_i,\theta_i), where \hat_i is an estimate of th ...
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