六西格玛黑带应该能够计算出一个分组发生频次分布数据的均值和标准偏差。
The Six Sigma Black Belt should be able to compute the mean and standard deviation from a grouped frequency distribution.
你可以用这种方法计算标准偏差。
特别是当标准偏差值很小的情况。
标准偏差如何量化度量值漂移？
How does the standard deviation quantify measurement scatter?
根据度量值计算出平均值和标准偏差。
From the measurements, compute the mean and standard deviation.
标准偏差又是怎么回事呢？
注意：不可能测量出“真实的”标准偏差。
Note: it may be impossible to measure the "true" standard deviation.
如果标准偏差值很大，那么它就比较分散。
实际上，末尾的警告指出标准偏差度量不精确。
In fact, the warning at the end says that the standard deviation was not accurately measured.
好消息是，用红线表示的标准偏差呈现下降趋势。
The good news is the standard deviation, the red line, is trending downward.
整体响应时间是1.4秒，并且标准偏差小于2秒。
Overall response time was 1.4 seconds with a standard deviation under 2 seconds.
执行时间的标准偏差估值点是645.586微秒。
A point estimate for the standard deviation of the execution time is 645.586 microseconds.
不仅减少了平均值，还将标准偏差减少到了1秒钟以下。
Not only have the averages decreased, but the standard deviation has decreased to below 1 second.
标准偏差总是不可能度量的（微基准测试的典型情况）。
The standard deviations were always impossible to measure (typical of microbenchmarks).
平均、最小、最大，以及标准偏差应答时间或者链接时间。
Average, minimum, maximum, and standard deviation response time or connection time.
那么这些异常值和分散度是怎么影响度量值和标准偏差的呢？
So how do these outliers and dispersion affect the values of the mean and standard deviation?
那么，如何用平均值和标准偏差判断两个任务中哪一个更快呢？
So, how can the mean and standard deviation be used to determine which of two tasks is faster?
那么，如果重复执行度量过程，平均值和标准偏差会有多大变化？
Then how different would the mean and standard deviation be if the procedure were repeated?
附加的标准偏差可能在分析结果中带来更高的百分比或者置信度。
Additional standard deviations can give you a higher percentage or confidence in the results of your analysis. A few of these confidence intervals are as follows.
您已经看到均值和标准偏差，直观上您可能会觉得温度是正态分布的。
You have seen the mean and standard deviation, and intuitively you might expect temperatures to be distributed normally. Let's check
在图11中，整体的平均响应时间下降了一半以上，并且标准偏差低于1秒。
In Figure 11, the overall average response time dropped by slightly more then half, with the standard deviation well under 1 second.
在异常值非常明显的数据集中，中间值和标准偏差值不应该用作位置的度量值。
In datasets where outliers are evident, the mean and standard should not be used exclusively as measures of location.
在最后的部分中，我们将会看到位置的当前度量；这指的就是度量值与标准偏差。
In our final section, we will look at how the current measures of location; that is, the mean and standard deviation and discuss how susceptible they are to dispersion.
正态分布数据认为，样本中约68%的数值分布在距离平均值为1的标准偏差之内。
Normally distributed data assumes that about 68% of the values in the sample are within 1 standard deviation of the mean.
因此，如果在数据集内存在异常值的话，那么度量值和标准偏差会得到相应的定位。
Thus, if outliers are present within the dataset the mean and standard deviation are located accordingly.
事实上，最近我和一个同事讨论了关于标准偏差的重要性，以及它常常被人忽视的情况。
Actually, a colleague and I recently discussed the importance of standard deviation and how it is often simply ignored.
标准偏差真正能够告诉您的是，某些用户是高于还是低于平均测试水平(或者算术含义)。
What the standard deviation really tells you is that some set of users were + or - the test average (or arithmetic mean).
事实上，计算标准偏差可能有点麻烦，但幸运的是，许多性能工具可以帮助您完成这个任务。
Actually calculating standard deviation can be a little challenging, but luckily many performance tools can help.
这是很重要的，因为它说明了，仅查看平均值是不够的，特别是当您的标准偏差非常大的时候。
This is important, because it shows that looking at just the average is not enough, especially if your standard deviation is a large number.
使用平均值和标准偏差，您可以说明，68%的尝试是在 0.2秒到 1.3 秒之间。
Using the average and the standard deviation, you can interpret that 68% of the attempts were between .2 and 1.3 seconds.
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