deviations

简明释义

英[/ˌdiːviˈeɪʃənz/]美[/ˌdiːviˈeɪʃənz/]

n. 差异，偏差（deviation 复数）

英英释义

单词用法

同义词

反义词

例句

1.The federal budget is perhaps the best indicator of the destructive path blazed by these policy deviations.

联邦预算可能是最好的指示器，指出了这些政策背离造成的破坏轨迹。

2.So, remember positive deviations came, let's put this back up there.

那么记得正的偏差来自于，我们把这个重新放到上面。

3.In recent years, many different methods for detecting deviations have been proposed.

近年来产生了很多不同的用于检测偏差的方法。

4.The Chinese used a gravity meter to correct for local deviations in sea level.

中国人用重力仪来校正海平面的局部偏差。

5.The standard deviations were always impossible to measure (typical of microbenchmarks).

标准偏差总是不可能度量的（微基准测试的典型情况）。

6.Sometimes such deviations are caused by faulty meters. Sometimes they are caused by leaks.

有时，这样的偏差是仪表错误造成，有时它就是由渗漏造成的。

7.These requirements can be measured, and deviations from them can then be prevented by means of process improvements or redesigns.

顾客的要求可以被量化，从而可以使得公司可以通过改进流程或者对产品的重新设计来避免产生偏差。

8.The study found several significant deviations 偏差 from the expected results.

这项研究发现结果与预期有几处显著的偏差。

9.In quality control, we monitor deviations 偏差 in production processes closely.

在质量控制中，我们密切监测生产过程中的偏差。

10.The report highlighted deviations 偏差 in the financial forecasts compared to actual figures.

报告强调了财务预测与实际数字之间的偏差。

11.Researchers are concerned about the deviations 偏差 in climate data over the past decade.

研究人员对过去十年气候数据中的偏差表示担忧。

12.The pilot was trained to handle deviations 偏差 from the flight path safely.

飞行员接受过处理飞行路径偏差的安全培训。

作文

In the realm of statistics and data analysis, the term deviations refers to the differences between observed values and their expected values. Understanding these deviations is crucial for interpreting data accurately and making informed decisions based on that data. When we analyze a set of data, we often calculate the mean or average value. The deviations from this mean can provide valuable insights into the variability and distribution of the data. For instance, in a classroom setting, if we measure the test scores of students, the deviations from the average score can help us understand how well students are performing relative to each other. In many cases, large deviations can indicate outliers—data points that differ significantly from others in the dataset. These outliers can skew our understanding of the overall trends and patterns. Therefore, it is essential to investigate these deviations further to determine whether they are due to errors, unique circumstances, or genuine variations in the data. For example, if one student scores exceptionally high or low compared to their peers, it might prompt educators to look into the reasons behind that student's performance, whether it be exceptional talent, lack of preparation, or external factors affecting their ability to perform. Moreover, in the context of quality control in manufacturing, deviations play a significant role in maintaining standards. Companies often set specific tolerances for their products, and any deviations from these standards can lead to defects. By monitoring these deviations, companies can identify areas where their processes may need improvement. For instance, if a factory produces metal parts that must meet precise measurements, any deviations from those measurements could result in parts that do not fit properly, leading to costly rework or customer dissatisfaction. Thus, understanding and controlling deviations is vital for ensuring product quality and customer satisfaction. Furthermore, in psychological studies, researchers often examine deviations in behavior to understand various mental health conditions. For example, if a group of individuals typically exhibits certain behaviors, any significant deviations from these behaviors may indicate an underlying issue that requires attention. This approach allows psychologists to identify abnormal patterns and develop appropriate interventions. In this way, deviations serve as critical indicators of change or concern in various fields, from education to manufacturing to psychology. In conclusion, the concept of deviations is fundamental across numerous disciplines. Whether we are analyzing data, ensuring product quality, or studying human behavior, recognizing and understanding deviations enables us to gain deeper insights and make better decisions. As we continue to encounter data in our increasingly quantitative world, the ability to interpret deviations will only become more important. By honing our skills in this area, we can enhance our analytical capabilities and apply them effectively in both professional and personal contexts.

在统计学和数据分析领域，术语deviations指的是观察值与其预期值之间的差异。理解这些deviations对于准确解读数据和基于该数据做出明智决策至关重要。当我们分析一组数据时，通常会计算出平均值或均值。从这个均值的deviations可以为我们提供关于数据的可变性和分布的重要见解。例如，在课堂环境中，如果我们测量学生的考试成绩，平均分数的deviations可以帮助我们理解学生之间的表现相对情况。 在许多情况下，大的deviations可能表明异常值——与数据集中其他点显著不同的数据点。这些异常值可能会扭曲我们对整体趋势和模式的理解。因此，进一步调查这些deviations是至关重要的，以确定它们是由于错误、独特情况还是数据中的真实变化。例如，如果一个学生的分数与同龄人相比异常高或低，这可能促使教育者研究该学生表现背后的原因，无论是卓越的才能、缺乏准备还是影响他们表现的外部因素。 此外，在制造业的质量控制中，deviations在维持标准方面也发挥着重要作用。公司通常为其产品设定特定的公差，任何deviations都可能导致缺陷。通过监测这些deviations，公司可以识别出其流程需要改进的领域。例如，如果一家工厂生产必须满足精确测量的金属零件，那么任何deviations都可能导致零件无法正确配合，从而导致昂贵的返工或客户不满。因此，理解和控制deviations对于确保产品质量和客户满意度至关重要。 此外，在心理学研究中，研究人员经常检查行为上的deviations以了解各种心理健康状况。例如，如果一组个体通常表现出某些行为，任何显著的deviations可能表明需要关注的潜在问题。这种方法使心理学家能够识别异常模式并制定适当的干预措施。通过这种方式，deviations作为各个领域（从教育到制造再到心理学）中变化或关注的重要指标。 总之，deviations的概念在众多学科中是基础性的。无论我们是在分析数据、确保产品质量还是研究人类行为，识别和理解deviations使我们能够获得更深入的见解并做出更好的决策。随着我们在日益量化的世界中继续接触数据，解读deviations的能力只会变得更加重要。通过提高我们在这一领域的技能，我们可以增强我们的分析能力，并在职业和个人环境中有效应用这些能力。