Answer:
Any data set's mean absolute deviation is the best way to understand how the spread data values correspond to each other and to the mean.
This value is obtained by calculating the difference between all data points and the data set's average, and then calculating the average of such by adding them all together and dividing by the total number of data points discovered.
If the mean absolute deviation is larger, it indicates that the data set has more variance, which is very common. Using the mean distances of data points from the mean to make inferences about data sets can be very useful.
Explanation:
