1·Clearly, we can see that the presence of outliers impacts these measures of location.
明显的是，我们可以看到异常值的存在影响到了位置的度量。
2·However, these parameters are less resistant to the presence of outliers.
但是，这些参数对异常值的存在有较低的抵抗力。
3·While the curve does not fit the data exactly, we can say that the data is a reasonable fit — however, we can see that a number of outliers are still present in the dataset.
当曲线不能精确地匹配数据时，我们可以说数据是有限匹配的 - 但是，我们可以看到有一系列的异常值仍然在数据集之内。
4·Identify potential skew and outliers.
识别潜在的歪斜和异常值。
5·The boxplot of the entire dataset showed there was significant right skew, a high level of dispersion and sizable quantity of outliers within our dataset.
整个数据集的箱线图显示了有很大程度的外泄，较高程度的分散度，以及数据集内大量的异常值。
1·These records are called outliers.
这些记录称为离群值。
2·First, if you only have a limited number of experts that are able to check outliers, you simply use the data records that belong to clusters with the highest deviation degree.
首先，如果检查离群值的专家有限，那么可以使用具有最高偏差度的集群的数据记录。
3·The following section provides a step-by-step example of how to find outliers with InfoSphere Warehouse and how to assign deviation degrees to individual data records.
接下来的小节将提供一个例子，以逐步演示如何用 InfoSphere Warehouse 发现离群值，以及如何为各个数据记录赋予偏差度。
4·To leverage the full potential of Cognos for displaying outliers, however, you need to employ some more advanced features.
但是，要想充分利用 Cognos 显示离群值的潜力，需要采用一些更高级的技巧。
5·The other way of dealing with outliers is to use a FIFO approach, or let the oldest times fall off the end, and let the newest times enter on the other end.
离群值的处理与其他的方法是使用一个FIFO的方法，或者让最古老的时代脱落，最后，让最新的时代另一端进入。
1·Then we can use the Score test to find the outliers in stock time series.
然后，我们可通过股票检测来发现股票时间序列的异常点。
2·Methods We introduce a robust principal component regression based on MVT and LMS to detect outliers, and compare methods using a practical example.
方法采用基于MVT和LMS方法的一种稳健主成分回归方法来诊断异常点，并结合实例进行方法的对比。
3·Besides, this article also proposes an effective method along with its detailed steps to test outliers.
另外，文中还提出了一种有效检验异常点的方法和步骤。
4·Firstly, we introduce the concept, cause and value of doing research in detecting outliers.
本文首先介绍了异常点的概念、成因及研究意义。
1·The experiment results show that LOG-ICBP has better predicting effect than ICBP when outliers exist.
实验结果表明LOG - IC BP网络在存在野值情况下的预测效果明显优于ICBP。
2·In this paper, we propose a technique for removing outliers based on the knowledge that correct trajectories are constrained to be in a subspace of their domain.
在本文中，我们提出一种技术用于去除野值的基础上在对知识，正确的轨迹约束的在他们的网域的子空间的。