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# notes 5 - Finding Outliers Using IQR Submitted by gfj100 on...

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Finding Outliers Using IQR Submitted by gfj100 on Fri, 10/30/2009 - 09:07 Some observations within our data set may fall outside the general scope of the remaining observations. Such observations are called outliers. To aid in determining whether any values in the data set can be considered outliers we can employ the IQR. Example : From the participation rates of the 9 South Atlantic states given above, we found an IQR of 10. Using this we can determine if any of the 9 observations can be considered outliers. We do this by setting up a “fence” around Q1 and Q3. Any values that fall outside this fence are considered outliers. To build this fence we take 1.5 times the IQR and then subtract this value from Q1 and add this value to Q3. This gives us minimum and maximum fence posts in which to compare the values of the data set. Q1 – 1.5*IQR = 64.5 – 1.5*10 = 64.5 – 15 = 49.5 Q3 + 1.5*IQR = 74.5 + 1.5*10 = 74.5 + 15 = 89.5 Comparing the 9 observations we see that the only data set value outside these fence points is 20 indicating that the observation value of 20 would be considered an outlier for this set of data.

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notes 5 - Finding Outliers Using IQR Submitted by gfj100 on...

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