-- Multiply the first averages by the number of observation for each set of these.
-- Add up the sets of averages.
-- Divide the sum by the total number of observations (Add cardinaility of each set).
-- The result is the average of the averages.
If you say have 4 "average" value and just add these, and divide by 4, the result is "unfair" because average may be of 3 observations, while another of 1000. So, to "compensate" and make every observation just as valuable, you re-generate the "sum of sums" and then divide by the total number of observations.
If all sets are the same you can divide by number of observations.
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adding up all the data and then dividing the sum by the number of data you had (for example, the average of the numbers 1, 2, and 3 is 1+2+3=6/3=2)
You can get the average of an average, but you would be averaging one number, so you would get the same result. You can get the average of a set of averages, though to make sense they will often need to be a weighted average of averages.
It is also an average. It is usually a better measure of the average value of the characteristic that is being measured.
No, a sum of averages is NOT as accurate as the average of the whole. For example: A=avg (1,10) = 5.5 B=avg (1, 1, 1, 1, 1) = 1 avg(A,B) = 3.25 [Average of averages] avg(1,1,1,1,1,1,10) = 2.29 [The original data set]
average is defined as a single value which has tendency to represent the data as a whole. averages are also called "measure of central tendency" or "measure of location"
by average we mean any measure of central tendency and mean is one of the averages. other measures of average are median ,mode, geomatric mean and harmonic mean.