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What does median absolute deviation tell you?
The median absolute deviation(MAD) is a robust measure of how spread out a set of data is. It is less affected by outliers because outliers have a smaller effect on the median than they do on the mean. The term median absolute deviation refers to a statistic calculated from a sample.
What the mean absolute deviation MAD is used for?
Mean absolute deviation (MAD) of a data set is the average distance between each data value and the mean. Mean absolute deviation is a way to describe variation in a data set. Mean absolute deviation helps us get a sense of how “spread out” the values in a data set are.
What does mean absolute deviation mean in forecasting?
Mean Absolute Deviation (MAD) measures the accuracy of the prediction by averaging the alleged error (the absolute value of each error). MAD is useful when measuring prediction errors in the same unit as the original series[5][6].
When to use the median absolute deviation to find outliers?
One more thing. If more than 50% of your data have identical values, your MAD will equal zero. All points in your dataset except those that equal the median will then be flagged as outliers, regardless of the level at which you’ve set your outlier cutoff.
What is the absolute deviation from the mean?
Let’s compare this to the absolute deviation from the mean in terms of the standard deviation: This time the distances from centre of the rightmost points are 1.88 and 3.67. These are at least 3.6 times the maximum distance of the remaining points.
How many outliers are there in a normal distribution?
We may expect that approximately 95% of points taken from a normal distribution are more than 2 standard deviations from the mean (and median), so 2 is a good outlier cutoff in this case. Okay, so what happens if you can’t assume that the underlying distribution is normal?
What happens if your Mad is more than 50%?
If more than 50% of your data have identical values, your MAD will equal zero. All points in your dataset except those that equal the median will then be flagged as outliers, regardless of the level at which you’ve set your outlier cutoff.