Minor changes vars.md

docs/en/vars.md

@@ -52,7 +52,7 @@ Percentiles can be plotted as a CDF curve or a timing diagram.
 
 ![img](../images/vars_4.png)
 
-The diagram above is a CDF curve. The vertical axis is the value of latency and the horizontal axis is the percentage of value less than the one at vertical axis. Obviously, this diagram is plotted by percentiles from 10% to 99.99%。 For example, the vertical axis value corresponding to the horizontal axis at 50% is 50%-percentile of the quantile value. CDF is short for [Cumulative Distribution Function](https://en.wikipedia.org/wiki/Cumulative_distribution_function).  When we choose a vertical axis value `x`, the corresponding horizontal axis means "the ratio of the value <= `x`".  If the numbers are randomly sampled, it stands for "*the probability* of value <= `x`”, which is exacly the definition of distribution.  The derivative of the CDF is a [PDF(probability density function)](https://en.wikipedia.org/wiki/Probability_density_function). In other words, if we divide the vertical axis of the CDF into a number of small segments, calculating the difference between the corresponding values at the at both ends and use the difference as a new horizontal axis, it would draw the PDF curve, just as the *(horizontal) normal distribution* or *Poisson distribution*. The density of median will be significantly higher than the long tail in PDF curve. However we care more about the long tail. As a result, most system tests show CDF curves rather than PDF curves.
+The diagram above is a CDF curve. The vertical axis is the value of latency and the horizontal axis is the percentage of value less than the corresponding value at vertical axis. Obviously, this diagram is plotted by percentiles from 10% to 99.99%。 For example, the vertical axis value corresponding to the horizontal axis at 50% is 50%-percentile of the quantile value. CDF is short for [Cumulative Distribution Function](https://en.wikipedia.org/wiki/Cumulative_distribution_function).  When we choose a vertical axis value `x`, the corresponding horizontal axis means "the ratio of the value <= `x`".  If the numbers are randomly sampled, it stands for "*the probability* of value <= `x`”, which is exacly the definition of distribution.  The derivative of the CDF is a [PDF(probability density function)](https://en.wikipedia.org/wiki/Probability_density_function). In other words, if we divide the vertical axis of the CDF into a number of small segments, calculating the difference between the corresponding values at the at both ends and use the difference as a new horizontal axis, it would draw the PDF curve, just as the *(horizontal) normal distribution* or *Poisson distribution*. The density of median will be significantly higher than the long tail in PDF curve. However we care more about the long tail. As a result, most system tests show CDF curves rather than PDF curves.
 
 Some simple rules to check if it is a *good* CDF curve
 
@@ -63,7 +63,7 @@ It is a good CDF curve if the gradient is small and the tail is narrow.
 
 ![img](../images/vars_5.png)
 
-It's a timing diagram of percentiles above, consisting of four curves. The horizontal axis is the time and the vertical axis is the latency. The curves from top to bottom show the timing disgram of latency at 99.9%/99%/90%/50%-percentiles. The color from top to bottom is also more and more shallow (from orange to yellow). You can move the mouse on over curves to read the corresponding data at different time. The number shows above means "The `99%`-percentile of latency before `39` seconds is `330` microseconds". The curve of 99.99% percentile is not counted in this diagram since it's usually significantly higher than the others which would make the other four curves hard to tell. You can click the bvars whose names end with "*_latency_9999*" to check the 99.99%-percentile along, and you can also check out curves of 50%,90%,99%,99.9% percentiles along in the same way. The timing digram shows the trends of percentiles, which is very helpful when you are analyzing the performance of the system.
+It's a timing diagram of percentiles above, consisting of four curves. The horizontal axis is the time and the vertical axis is the latency. The curves from top to bottom show the timing disgram of latency at 99.9%/99%/90%/50%-percentiles. The color from top to bottom is also more and more shallow (from orange to yellow). You can move the mouse on over curves to get the corresponding data at different time. The number showed above means "The `99%`-percentile of latency before `39` seconds is `330` microseconds". The curve of 99.99% percentile is not counted in this diagram since it's usually significantly higher than the others which would make the other four curves hard to tell. You can click the bvars whose names end with "*_latency_9999*" to check the 99.99%-percentile along, and you can also check out curves of 50%,90%,99%,99.9% percentiles along in the same way. The timing digram shows the trends of percentiles, which is very helpful when you are analyzing the performance of the system.
 
 brpc calculates latency distributed of the services. Users don't need to do this by themselves. The result is like the following piecture.