Find the inter quartile range, which is IQR = Q3 - Q1, where Q3 is the third quartile and Q1 is the first quartile. Then find these two numbers:
a) Q1 - 1.5*IQR
b) Q3 + 1.5*IQR
Any observation that is below a) or above b) can be considered an outlier.
edit: Chadwick, quartiles are considered robust, meaning that they are not highly effected by outliers. This is because it takes location into account, not the values. Let's look at your data set (sorted).
2 3 6 9 13 18 21 106
position of Q1 = (8+1)/4 = 2.25
Q1 = 0.75(3)+0.25(6) = 3.75
position of Q2 = (8+1)/2 = 4.5
Q2 = (9+13)/2 = 11
position of Q3 = 3(8+1)/4 = 6.75
Q3 = 0.25(18)+0.75(21) = 20.25
Notice that none of these actually use the value 106. Let's continue.
So IQR = Q3-Q1 = 20.25-3.75 = 16.5
Q1-1.5*IQR = 3.75-1.5*16.5 = -21
Q3+1.5*IQR = 20.25+1.5*16.5 = 45
No numbers are below -21, but 106 is above 45, so it can be considered an outlier.
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It is the biggest or lowest number out of the set. The number would be way different than the rest. Example: 14 12 52 17 19 The outlier would be 52 because it is the biggest number. Example: 59, 42, 77, 65, 10 The outlier would be 10 because it is the lowest number.
5. This is the number which is the furthest from the others.
A number that is different from any other numbers in the data.!
0s are not the outlier values
The answer depends on the nature of the outlier. Removing a very small outlier will increase the mean while removing a large outlier will reduce the mean.