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| == Percentiles of exponential data and use in outlier detection == | |
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| - (Mean score) (LN(1- PERC/100 ) gives the threshold of the PERC% percentile. | - (Mean score) (LN(1- PERC/100 )) gives the threshold of the PERC% percentile. |
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| This follows from P(X < x) = 1 – EXP(-R*score) where rate, R, is estimated from the sample data by 1/mean. | This follows from P(X < x) = 1 – EXP(-R*x) where rate, R, is estimated from the sample data by 1/mean. These percentiles may be [[http://www.statsoft.com/textbook/glose.html|used]] to classify a data point as extreme since outliers are defined as either > 75th percentile + 1.5 * (75th perc - 25th perc) or < 25th percentile - 1.5 * (75th perc - 25th perc) |
Percentiles of exponential data and use in outlier detection
If the data follows an exponential distribution (test this by using Kolmogorov-Smirnov test in SPSS and choosing the exponential option). Then
- (Mean score) (LN(1- PERC/100 )) gives the threshold of the PERC% percentile.
This follows from
P(X < x) = 1 – EXP(-R*x) where rate, R, is estimated from the sample data by 1/mean.
These percentiles may be used to classify a data point as extreme since outliers are defined as either
> 75th percentile + 1.5 * (75th perc - 25th perc)
or
< 25th percentile - 1.5 * (75th perc - 25th perc)