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。
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。
Figure six shows a boxplot of our data with the outliers removed.
图6显示了异常值排除之后的数据箱线图。
This data can now be used to determine the percentage of outliers for each page and troubleshoot potential bottlenecks.
现在您可以使用该数据来决定每一页面和故障排除潜在瓶颈的百分比。
Deviation detection is a highly interactive task, and outliers must usually be checked manually to see whether they indicate fraud, errors in the data, or some interesting opportunity.
偏差检测是高度交互性的任务，通常需要手动检查离群值，以查明是否存在欺诈倾向、数据错误或者潜在的机遇。
A few outliers skew the data, they say.
他们表示：少数外行曲解了研究数据。
The reason why the graph displays these values as outliers is that due to the calculations that take place behind the scenes these values are deemed too distant from the majority of the data.
为什么图将这些值显示为异常值的原因，在于在这样一幅场景的背后存在这样一种考虑，就是这些值远离了大多数的数据。
Part of the process is removing rogue data points that we call 'statistical outliers' in order to simplify the results picture.
部分过程的数据点已经被移除，为的是让结果更加清晰。
Based on the relationship of these two variables, we derive outliers from our sample data.
基于这两个变量之间的关系，我们从我们的样本数据中得出的异常值。
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InfoSphereWarehouse发现离群值，以及如何为各个数据记录赋予偏差度。
In the following, learn how InfoSphere Warehouse detects outliers and how you can apply deviation detection to your data.
接下来将了解InfoSphere Warehouse如何检测离群值，以及如何对数据应用偏差检测。
To determine the percentage of such outliers, you need the raw data for the test run that Rational performance Tester USES to plot the performance report graphs. These are the steps to get this data.
为了决定百分比，对于RationalPerformance Tester的运行您需要原始数据，以作出性能报告的图。
Official data (typically released a few months after the year end) generally match our consensus forecasts, though some outliers distort the average.
官方数据(一般在年底之后的几个月发布)通常符合我们的普遍预测，尽管某些个例会扭曲平均值。
In the final chapter, we mine stock trading data using time series method, find out the model and outliers in the data and, at last, we show the more exact forecasting model and outlier mining method.
第五章利用时间序列的方法对证券交易数据进行了挖掘，找出了数据中的模式和异常，相对传统方法而言，给出了更精确的预测模型和异常挖掘方法。
Outliers and noise will cause difficulties during processing and using flight data.
飞行数据因为野点和噪声的存在给其进一步处理和利用造成了困难。
Excluding data outliers, the deviation from the normal data filtering, to reach the desired data results.
说明：剔除数据异常值，将偏离正常值的数据滤除，使其达到所需的数据效果。
When we analyse seasonal series of hospital data, there are missing values and outliers. It is difficulty for forecasting.
医院季节性时间序列分析中，会出现缺失数据和异常值，这就影响了预测预报。
Find microarrays in a data set which are outliers in this distribution.
找出分布中异常的资料组的微阵列。
Therefore, the identification of outliers in panel data model is a very important work.
因此该面板数据模型异常值的识别问题是一项重要的研究工作。
In the stage I, called as data preprocessing, the support vector machines for regression (SVMR) approach is used to filter out the outliers in the training data set.
第一阶段称为所谓的资料预先处理，即使用支援向量回归来找出训练资料集中的离异点并删除之。
Experiments on simulated and real image data are conducted. The results show that this algorithm is very robust to noises and outliers, and the fundamental matrix with high accuracy can be found.
大量的模拟数据和真实图像的实验结果表明，此算法不仅具有良好的鲁棒性，而且可提高基础矩阵的估计精度。
In this thesis, the author presents the theory of data mining, and deeply analyzes the algorithms of clustering and outliers detection.
本文介绍了数据挖掘理论，对聚类及孤立点检测算法进行了深入地分析研究。
Data processing of flight test indicates that this method is good for the process of measurement with a series of outliers.
飞行试验数据处理结果表明，该方法对含有斑点型异值的航迹测量数据有很好的处理效果。
A new method is presented to identify outliers in load data by fully utilizing the features of electrical load curves.
电力负荷坏数据辨识应充分考虑负荷曲线本身的特征。
The studies on outliers and volatilities have become one of the core contents on macroeconomic data quality.
对于宏观经济统计数据的异常性和波动性进行分析，已成为研究数据质量的最核心内容之一。
For example, plots of principal component scores can help identify outliers in the data when they exist.
例如，主成分得分的情节，可以帮助鉴别孤立点数据，当他们存在。
That's the summary for conditional formatting and PivotTables. With these improvements, PivotTables can now be used as a great tool for exploring data, highlighting trends, spotting outliers, etc.
这是条件格式和数据透视表的总结。有了这些改进，数据透视表可以作为一个强大的数据研究，突出趋势，发现突出者，等等的工具来使用了。
Conclusion it is necessary to carry out robust estimation to generalized additive models when there are outliers in data.
结论在离群点存在时对广义可加模型进行稳健估计是必要的。
This paper proposed a new prediction method for outliers over data stream based on sparse representation to improve the optimum prediction speed and performance of outliers over data stream.
为了提高数据流中异常数据的预测速度与精度，提出一种基于稀疏表示的数据流异常数据预测方法。
This paper proposed a new prediction method for outliers over data stream based on sparse representation to improve the optimum prediction speed and performance of outliers over data stream.
为了提高数据流中异常数据的预测速度与精度，提出一种基于稀疏表示的数据流异常数据预测方法。
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