The mean deviation is calculated as:
| MD = \frac{\displaystyle\sum_{i=1}^{n}\mid x_i-\bar{x}\mid}{n-1} |
i.e. the average of the absolute differences between the data points and their mean. This can be thought of as the expected distance that the variable will actually be from the mean. The mean deviation offers two potential advantages over the other measures of spread: it has the same units as the output; and it gives equal weighting to all generated data points.
