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What are percentiles and what differences do they make in PRTG reports?

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In PRTG reports, the statistics can be shown as percentiles. But I don't know what percentiles are, how PRTG calculates them and what kind of effect they make on the data I get.

historic-data percentiles prtg reports

Created on Oct 1, 2010 2:23:17 PM by  Daniel Zobel [Paessler Support]

Last change on Mar 3, 2015 3:19:55 PM by  Martina Wittmann [Paessler Support]



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This article applies to PRTG Network Monitor 14 or later

Percentiles in PRTG Reports

In your PRTG reports you get statistically condensed information about the data that various sensors collect. In the sensor report settings you can choose to get percentile calculation, as opposed for example to raw data or normal percentages.

Percentiles can be computed according to various mathematical methods.

How Percentiles Are Calculated in PRTG

  1. Sort the measurements: The values of a data sample have to be ordered in a row from the lowest to the highest. You get the individual values and the number of rows N.
  2. Compute the row numbers of the percentile value RN using the percentile value P and the number of rows N:
    RN = 1 + ((N-1) * P)
    There are two types of RNs: FRN = floor (RN) and CRN = ceiling (RN)
  3. Determine the result:
    If (CRN = FRN = RN)
    then (value of expression from row at RN)
    else (value of expression for row at FRN) + (RN - FRN) * (CRN row - FRN row value)
    Note: You can define if you would like to get the then or the else result. If in the report percentile settings you choose Continuous, the result will always be interpolated as in the else part. If you choose Discrete, then the next smaller discrete value, which is the FRN, is taken as a result, according to the then part.

Example

A 95th-percentile says that in 95% of the cases, the data is below a certain value whilst in only 5% of the cases, the data will be above a certain value.

  • Step 1: Let's assume we have the following number of rows and measured values:
Row Number12345678
Value1372125266672
  • Step 2: RN = 1 + ((8-1) * 0.95) = 7,65 with FRN = 7 and CRN = 8
  • Step 3: Since CRN = FRN = RN is not true, we calculate the else-part:
    Percentile value = 66 + ((7,65-7)* (72-66)) = 69.9
    So 69.9 is our continuous 95th-percentile. If we want to have a discrete 95th-percentile value, it would be the FRN which is 66.

As you can see, in a 95th-percentile calculation of our measurements the value number 8 (72) would be discarded because it belongs to the 5% of peaks. In our case this is true for the 95th continuous percentile as well as for the 95th discrete percentile.

Advantage and Disadvantage of Percentiles

The main advantage of using percentiles is that unusually high values (like whiskers in boxplots) are not included into the averaging calculations. This means that the statistics include more relevant data. In the example of the 95th-percentile, 5% of the highest measured values are discarded for the statistical report.

If you prefer not to exclude extremes, because you don't want to disguise the effect they make on your monitored IT infrastructure, do not choose percentiles in the report settings.

Note: For more details on this percentile calculation, please see archive.org.

Created on Mar 3, 2015 4:24:30 PM by  Martina Wittmann [Paessler Support]

Last change on May 20, 2016 11:26:27 AM by  Gerald Schoch [Paessler Support]



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Should say Column Number instead of Row Number .... ?

Created on May 20, 2016 5:38:08 AM by  Raymond Leung (0)



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And Percentile value = 66 + ((7,95-7)* (72-66)) = 69.9 should be Percentile value = 66 + ((7.65-7)* (72-66)) = 69.9

Created on May 20, 2016 5:40:57 AM by  Raymond Leung (0)



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Hi Raymond,

Thank you, I fixed the typo. "Row Number" does not mean "table row" but refers to an ordered row of values, so it is correct here. The table in the article just shows values in a row in this format, it is not a "data table".

Regards,

Created on May 20, 2016 11:34:41 AM by  Gerald Schoch [Paessler Support]



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