skewing

简明释义

英[ˈskjuːɪŋ]美[ˈskjuːɪŋ]

n. 偏移

v. 歪斜（skew 的 ing 形式）

英英释义

单词用法

同义词

反义词

例句

1.If there is a slight skewing favoring an empty queue over a full one, the pipe will rarely fill and the reader will more often receive fresh Numbers.

如果有轻微的扭曲支持一个空的队列满的，管道将很少填补和读者经常会收到新的号码。

2.Weinberg’s research has shown that in occupations in which “people skills” are becoming more important, jobs are skewing toward women. And that category is large indeed.

魏因贝格的研究表明，在重视“社交技能”的工作中，职位在向女性倾斜，而且存在大量这类工作。

3.As you can see from the output, the data has some extensive skewing, and data volumes in some database partitions are 60% skewed.

正如从结果中看到的，数据大量倾斜，某些数据库分区内的数据量居然有60%的倾斜。

4.Its features include splitting pages, deskewing (or skewing), content boxes, margin editing, alignment, and output editing.

它的功能包括页拆分，倾斜校正（也叫纠偏），内容框，编辑页边空白，对齐和编辑输出。

5.Using Photoshop lens correction filters to adjust the perspective and some manual skewing and tweaking I was able to create the composite background texture reasonably fast.

使用PS镜头校正过滤器来调整透视和一些人工倾斜和扭曲，我这样我可以快速合理地创建混合背景材质。

6.Although exposure compensation is disabled in manual, its function can be duplicated by skewing the ISO setting as necessary.

尽管曝光补偿在手动模式被停用，它的功能可由设定必要的ISO替代。

7.Studies show that, partly because of unsettled trades, more than one party sometimes claims ownership of a stock, skewing shareholder votes.

调查表明，部分出于未付款交易的原因，有时不止一方宣布对同一支股票的所有权，这扭曲了股东的投票结果。

8.Averages can include data for service startup or other events that might cause the Numbers to go far out of range for a brief period, thereby skewing results.

平均值也许会包括服务启动或其他事件的数据，而这些事件可能导致数字在短时间内远远超出范围，从而引起结果扭曲。

9.The graph shows a skewing effect due to the extreme values on the right.

图表显示由于右侧极端值的影响，出现了扭曲效应。

10.The survey's sample size was too small, skewing the overall findings.

调查的样本量太小，扭曲了整体发现。

11.The recent data collection was affected by bias, skewing the results significantly.

最近的数据收集受到偏见的影响，扭曲了结果。

12.His personal experiences are skewing his perception of the situation.

他的个人经历正在扭曲他对情况的看法。

13.In statistics, outliers can cause a problem by skewing the mean.

在统计学中，异常值可能会通过扭曲平均值来造成问题。

作文

In the realm of statistics and data analysis, the concept of skewing is crucial for understanding how data can be represented and interpreted. Skewing refers to the asymmetry in a distribution, where one tail is longer or fatter than the other. This phenomenon can significantly impact the results of statistical analyses and lead to misleading conclusions if not properly addressed.For instance, consider a scenario where a researcher is analyzing the income levels of a particular population. If the income distribution is skewed, it means that there are a few individuals with extremely high incomes that pull the average income up, resulting in a misleading representation of the typical income level. In this case, the median income would provide a more accurate picture, as it is less affected by outliers. Thus, recognizing skewing is essential for selecting the appropriate measures of central tendency and dispersion when interpreting data.Moreover, skewing can occur in various forms, such as positive skewness, where the tail on the right side is longer, and negative skewness, where the left tail is longer. Understanding the direction of skewing helps analysts make informed decisions about data transformations that may be necessary to meet the assumptions of certain statistical tests. For example, in cases of positive skewing, applying a logarithmic transformation can help normalize the data, making it suitable for parametric tests.Beyond statistics, skewing also finds relevance in fields like psychology and social sciences. Researchers often face challenges when their survey data is skewed due to response biases. For example, if a survey on job satisfaction receives disproportionately high ratings from employees in a highly satisfied department, the overall results may not accurately reflect the true sentiment across the organization. Recognizing such skewing in responses allows researchers to adjust their analysis and interpretations accordingly, ensuring that conclusions drawn are representative of the larger population.In conclusion, the concept of skewing plays a vital role in data analysis across various disciplines. It serves as a reminder that raw data often requires careful examination and transformation to ensure accurate interpretations. By acknowledging the presence of skewing and adjusting our analytical approaches, we can avoid pitfalls that lead to erroneous conclusions. Ultimately, a thorough understanding of skewing empowers researchers and analysts to present findings that truly reflect the complexities of the data at hand, allowing for more informed decision-making in both academic and practical contexts.

在统计学和数据分析领域，skewing的概念对于理解数据如何被表示和解释至关重要。Skewing指的是分布的不对称性，其中一个尾部比另一个更长或更胖。这种现象可以显著影响统计分析的结果，如果没有正确处理，可能导致误导性的结论。例如，考虑一个研究者分析特定人群收入水平的场景。如果收入分布是skewed，这意味着有少数个体的收入极高，拉高了平均收入，导致对典型收入水平的误导性表现。在这种情况下，中位数收入将提供更准确的图景，因为它不易受到异常值的影响。因此，认识到skewing对于选择适当的集中趋势和离散度量非常重要，以便在解释数据时做出明智的决策。此外，skewing可以以各种形式出现，例如正偏态，其右侧的尾巴更长，以及负偏态，其左侧的尾巴更长。理解skewing的方向帮助分析师做出关于可能需要的数据变换的明智决策，以满足某些统计测试的假设。例如，在正skewing的情况下，应用对数变换可以帮助规范化数据，使其适合参数测试。除了统计学，skewing在心理学和社会科学等领域也具有相关性。研究人员在面对由于反应偏差而导致的调查数据skewed时常常面临挑战。例如，如果一项关于工作满意度的调查从一个高度满意的部门收到不成比例的高评分，那么整体结果可能无法准确反映整个组织的真实情感。识别这种反应中的skewing使研究人员能够相应地调整他们的分析和解释，确保得出的结论能够代表更大的人群。总之，skewing的概念在各个学科的数据分析中发挥着至关重要的作用。它提醒我们，原始数据往往需要仔细检查和变换，以确保准确的解释。通过承认skewing的存在并调整我们的分析方法，我们可以避免导致错误结论的陷阱。最终，深入理解skewing使研究人员和分析师能够呈现真正反映数据复杂性的发现，从而在学术和实际背景中做出更明智的决策。