Mean Absolute Deviation

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  The mean absolute deviation or MAD as it is often abbreviated, of a dataset is the average or "mean" distance between each data point from the data set and the mean/average.  This will give you an idea of the amount of variability in a dataset.  MAD can be calculated from both observations and the arithmetic average (mean) of those observations.  Another option when calculating MAD is to calculate based on actual data minus forecasted data.  If forecasting or being used for future analysis you can apply different weights to your forecast or use exponential smoothing in an attempt to make your forecast more accurate.  In most cases however mean absolute deviation uses actual data.

How to Calculate Mean Absolute Deviation

1. ​Collect and Document your Data Set.
2. Find the Mean (Average) Value of your Dataset.
3. Calculate the Absolute Deviations or Distance from the Mean or Average.
4. Calculate the Mean Absolute Deviation this is Done by Averaging the Absolute Deviations.

Example of Mean Absolute Deviation
  The marketing team has been posting pictures and videos online as part of a campaign. The last 6 campaign posts received the following number of likes: 15,11,13,16,18 and 17.  

1. ​Collect and Document Data: 15,11,13,16,18 and 17.
2. Find the mean value of the dataset: 15
3. Calculate the absolute deviations or distance from the mean: 0,4,2,1,3 and 2.
4. Calculate the mean absolute deviation: 2

Challenge:

• Recently the marketing team has launched another campaign.  The campaign consisted of 8 posts on different platforms.  The collected the number of engagements for each posts, which were the following: 10,14,18,8,12,20,24 and 11.  What is the Mean Absolute Deviation of the Campaign Posts?  Share your thoughts in the comments section below and receive a coupon to one of Lean Strategies International LLC's courses.