Airlines overbook flights on the knowledge that some customers will cancel or change their flights after booking them. Knowing how varied is the distribution assists airlines in selling enough tickets.
Well lets say Company Alpha makes shoes. Now this company is a very smart company and only wants to make shoes that fit aproxximately 85% of the populations shoes, to avoid marketing unpopular or unsellable shoe sizes. Well, how would you do this. First you organize dataof peoples shoe sizes, then make a chart showing the approximate range. Now without SDEV that would be nearly impossible since no1 particular group is universal, it is always good to use SDEV in those situations. OK now lets say company alpha has the nice little chart in their hands, and lo and behold! it is in the shape of a bell...weird ;). Anyways, how would they get the shoe sizes that 85% of the people wear. This is where the VAR helps out. Now lets say this chart was taken from a state called COWBOYISLAND, and people form this state are notoriously larger than any other states. Ok now we have a problem, not only does this chart show innacurate data, but the population density for the state of COWBOYISLAND is low compared to other states. You can normalize the data by applying the variance rules, which take into accoun trends across the WHOLE data range
Mar 16th, 2015
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