outliers

简明释义

英[ˈaʊtˌlaɪəz]美[aʊtˈlaɪərz]

n. 离开本体的东西；老围层；异常值，离群值（outlier 的复数）

英英释义

单词用法

同义词

反义词

例句

1.There are always outliers.

总是会有例外的。

2.With further analysis of these outliers we saw a significant number of poorly performing transactions.

对这些异常值进一步分析之后，我们会看到大量执行糟糕的交易。

3.Malcolm Gladwell cleverly subtitled his third book, “Outliers”, “The Story of Success”.

马尔科姆•葛拉威尔为他的第三本书《异类：不一样的成功启示录》起了个讨巧的副标题。

4.When the outliers were excluded, a new boxplot was created.

当异常值被排除在外时，会创建一个新的箱线图。

5.However, none of these three methods are particularly resistant to outliers.

但是，这三种方法中没有一种对于异常值有较强的抵抗力。

6.So how do these outliers and dispersion affect the values of the mean and standard deviation?

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？

7.Why are the British such outliers?

为什么英国人如此置身其外？

8.The machine learning model performed poorly due to the presence of several outliers 异常值 in the training data.

由于训练数据中存在几个outliers 异常值，机器学习模型表现不佳。

9.When plotting the graph, we noticed a few outliers 异常值 that did not fit the overall trend.

在绘制图表时，我们注意到一些outliers 异常值不符合整体趋势。

10.The finance team flagged several outliers 异常值 in the expense reports that required further investigation.

财务团队标记了几项outliers 异常值在费用报告中，需要进一步调查。

11.In data analysis, we often need to identify outliers 异常值 that can skew our results.

在数据分析中，我们经常需要识别outliers 异常值，因为它们可能会扭曲我们的结果。

12.Statisticians often use robust methods to minimize the impact of outliers 异常值 in their calculations.

统计学家通常使用稳健的方法来最小化outliers 异常值对计算的影响。

作文

In the field of statistics and data analysis, the term outliers refers to data points that deviate significantly from the rest of the dataset. These outliers can arise due to variability in the measurement or may indicate experimental errors. Understanding outliers is crucial for accurate data interpretation and analysis. For instance, when conducting a study on students' test scores, if one student scores exceptionally high or low compared to their peers, that score would be considered an outlier. Ignoring this outlier could lead to misleading conclusions about the performance of the entire group.The presence of outliers can heavily influence statistical calculations such as the mean and standard deviation. If we take the average of a set of numbers that includes an outlier, the result may not accurately reflect the central tendency of the data. For example, if most students score between 70 and 90 on a test, but one student scores 150, including this outlier would skew the average upwards. This is why many researchers use measures like the median, which is less affected by outliers, to summarize their data.Moreover, outliers can provide valuable insights into unusual phenomena or errors in data collection. For example, in a medical study examining the effects of a new drug, if one participant experiences an extreme reaction that is not seen in others, this outlier may warrant further investigation. It could reveal important information about the drug’s side effects or the individual’s unique response to treatment.Identifying outliers is an essential step in the data analysis process. Various methods exist to detect these anomalous values, such as the Z-score method, where any data point with a Z-score greater than 3 or less than -3 is considered an outlier. Another common method is the interquartile range (IQR), which identifies outliers as any points lying outside 1.5 times the IQR above the third quartile or below the first quartile. However, it is important to approach outliers with caution. Not all outliers should be removed from a dataset; some may represent valid observations that are simply rare. The decision to exclude an outlier should be based on a thorough understanding of the context and potential reasons behind its occurrence. In some cases, retaining outliers can enhance the robustness of the analysis by showcasing the full range of variability in the data.In conclusion, outliers play a significant role in data analysis, influencing statistical outcomes and providing insights into anomalies. While they can complicate data interpretation, they also offer opportunities for deeper investigation. Researchers must carefully consider how to handle outliers to ensure the integrity and accuracy of their findings. By understanding and appropriately addressing outliers, analysts can derive more meaningful conclusions from their data, ultimately leading to better decision-making and insights in various fields, from education to healthcare and beyond.

在统计学和数据分析领域，术语outliers指的是与数据集其余部分显著偏离的数据点。这些outliers可能由于测量的变异性而产生，或可能表明实验错误。理解outliers对准确的数据解释和分析至关重要。例如，在对学生考试成绩进行研究时，如果某个学生的分数与同龄人相比异常高或低，则该分数将被视为outlier。忽略这个outlier可能会导致对整个组的表现得出误导性的结论。outliers的存在可能会严重影响统计计算，例如均值和标准差。如果我们计算一个包含outlier的数据集的平均值，那么结果可能无法准确反映数据的集中趋势。例如，如果大多数学生的考试分数在70到90之间，但有一名学生得了150分，那么包括这个outlier会使平均值向上偏移。因此，许多研究人员使用像中位数这样的度量来总结他们的数据，因为中位数不太受outliers的影响。此外，outliers可以提供有关异常现象或数据收集错误的宝贵见解。例如，在一项研究新药效果的医学研究中，如果一名参与者经历了与其他人不同的极端反应，这个outlier可能需要进一步调查。它可能揭示有关药物副作用或个体对治疗的独特反应的重要信息。识别outliers是数据分析过程中的一个基本步骤。存在多种方法来检测这些异常值，例如Z-score方法，其中任何Z-score大于3或小于-3的数据点都被视为outlier。另一种常用方法是四分位距（IQR），它将outliers定义为位于第三四分位数以上或第一四分位数以下1.5倍IQR之外的任何点。然而，处理outliers时必须谨慎。并非所有的outliers都应从数据集中删除；有些可能代表有效观察，只是比较罕见。排除outlier的决定应基于对其发生的上下文和潜在原因的透彻理解。在某些情况下，保留outliers可以增强分析的稳健性，展示数据的全范围变异性。总之，outliers在数据分析中发挥着重要作用，影响统计结果并提供关于异常的见解。虽然它们可能会使数据解释变得复杂，但它们也提供了更深入调查的机会。研究人员必须仔细考虑如何处理outliers，以确保他们发现的完整性和准确性。通过理解和适当地处理outliers，分析人员可以从数据中得出更有意义的结论，最终在教育、医疗等各个领域做出更好的决策和洞察。