Returns the mean absolute difference of the input data series.
Syntax
MD(X)
X is the input data sample (one/two dimensional array of cells (e.g. rows or columns))
Remarks
 The input time series data may include missing values (e.g. #N/A, #VALUE!, #NUM!, empty cell), but they will not be included in the calculations.
 The sample mean difference (MD) is computed as follows:
$$ \Delta = \mathrm{MD} = \frac{\sum_{i=1}^n \sum_{j=1}^n \ x_i  x_j \}{n \times \left ( n1 \right )} $$
Where:
 $x_i$ is the value of the ith nonmissing observation
 $n$ is the number of nonmissing observations in the sample
 The mean absolute difference is the product of the sample mean and the relative mean difference (RMD) and so can also be expressed in terms of the Gini coefficient :
$$\mathrm{MD}= 2 \times G \times \bar{x}$$
Where:
 $\bar{x}$ is the arithmetic sample mean
 $G$ is the Gini Coefficient
 Because of its ties to the Gini coefficient, the mean difference is also called the "Gini mean difference." It is also known as the "Absolute mean difference."
 The sample mean difference is not dependent on a specific measure of central tendency like the standard deviation.
 The mean difference of a sample is an unbiased and consistent estimator of the population mean difference.
Examples
Example 1:


Formula  Description (Result)  

=MD($B$2:$B$30)  Mean difference (1.452) 
Files Examples
References
 Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0691042896
 Tsay, Ruey S.; Analysis of Financial Time Series John Wiley & SONS. (2005), ISBN 0471690740
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