About 90 percent in Analyzer

Let's take a look at the following official description:

Solution: the 90th percentile is the value for which 90% of the data points are smaller

The 90th percentile is a measure of stastical distribution, not unlike the median. the median is the middle value. the median is the value for which 50% of the values were bigger, and 50% smaller. the 90th percentile tells you the value for which 90% of the data points are smaller and 10% are bigger.

Statistically, to calculate the 90th percentile value:
1. Sort the transaction instances by their value.
2. Remove the top 10% instances.
3. The highest value left is the 90th percentile.

Example:
There are ten instances of transaction "T1" with the values, (in sec ).
1. sort by value -- 1, 2, 3, 4, 5, 6, 7, 8, 9, 20.
2. Remove top 10% -- remove the value "20 ."
3. The highest value left is the 90th percentile -- 9 is the 90th percentile value.

The 90th percentile value answers the question, "What percentage of my transactions have a response time less than or equal to the 90th percentile value? "Given the above information, here is how LoadRunner calculates the 90th percentile.

In analytics 6.5:
The values for the transaction are ordered in a list.
The 90% is taken from the ordered list of values. The place from which it is taken is

Rounding to the small value the number: 0.9 * (number of values-1) + 1

In Analysis 7 and above:
Each value is counted in a range of values. for example, 5 can be counted in a range of 4.95 to 5.05, 7.2 in a range of 7.15 to 7.25. the 90% is taken from the range of values that the number of transaction in it and before it is >=( 0.9 * Number of values ).

This difference in the methods can lead to different 90% values. again, both methods lead to correct values as defined by the 90th percentile. however, the algorithm to calculate these figures has changed in LoadRunner 7 and above.

According to my understanding, this is just a parameter for calculating the response time. Among the running times of this firm, 90% of the Times falls in the response time. This parameter can be modified in tools-option to define a response time range, for example:
A transaction runs for 10 times as follows:
12.3 15.4 15.9 16.6 17.8 18.5 19.5 20.5 21.5 (I am writing a number casually)
Then the 90 percent value is 21.5, that is, 10% response times are greater than it, and 90% of the time is less than or equal to it.

So what is the role of the Time-defined statistical value?

I think like this. If this value is very large (for example, 90% here), it only shows that many transactions have a large response time, which is a vague and general value. If I define 50% and get a small value, what does it mean for performance?
If we only want to analyze the points greater than this defined value, that is, which response time is greater than this value (because it can be defined, we can tune it down when it is large, again, we can only analyze the cause of the response time above this point and analyze the bottleneck of the system as soon as possible (of course, this is not certain, because the bottleneck analysis depends on a lot of information ).