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80, 71, 81, 99, 1, 54, 55, 16, 20, 27, 61, 62, 79, 68, 35, 37, 38, 41, 45, 49, 50, 21, 27, 50, 51, 55, 55, 60, 61, 70 PROBLEM #10.
80, 71, 81, 99, 1, 54, 55, 16, 20, 27, 61, 62, 79, 68, 35, 37, 38, 41, 45, 49, 50, 21, 27, 50, 51, 55, 55, 60, 61, 70
PROBLEM #10. The last concept we want to cover is "rare events" or "unusual values". How do we identify them? This does NOT mean that if a data point is "unusual" we can simply delete it. Any deletions MUST be justifiable. Imagine if a pharmaceutical company deleted the one death in 100 patients taking a new medication because it was "unusual". Want to take that pill?
An UNUSUAL data point can be characterized two ways (for us) DO BOTH FOR OUR 30 DATA POINTS:
(a) Any data point that is more than TWO (2) standard deviations ABOVE OR BELOW the mean.
(b) Any data point that is more than Q3 + (1.5 * IQR) ABOVE OR BELOW the mean.
(c) ARE ANY DATA POINTS "UNUSUAL"?
Next week we move into PROBABILITY which is the basis for INFERENTIAL STATISTICS. This allows us to test hypotheses about a POPULATION, based on samples taken from that population. This is what statistics is all about. It starts with PROBABILITY, meaning that life is a "crap shoot". Failing to realize that, we have the famous last words: "What could go wrong?"
Can someone breakdown this probelm for me?
