mean relative deviation

简明释义

平均相对偏差

英英释义

例句

1.In quality control, calculating the mean relative deviation 均值相对偏差 helps assess the consistency of production processes.

在质量控制中，计算均值相对偏差 均值相对偏差 有助于评估生产过程的一致性。

2.Researchers found that the mean relative deviation 均值相对偏差 of their measurements was surprisingly low.

研究人员发现，他们测量的均值相对偏差 均值相对偏差 意外地低。

3.The mean relative deviation 均值相对偏差 of the dataset indicates how much the values deviate from the average.

该数据集的均值相对偏差 均值相对偏差 表明数值与平均值的偏离程度。

4.To improve accuracy, the team aimed to reduce the mean relative deviation 均值相对偏差 in their experimental results.

为了提高准确性，团队旨在减少他们实验结果中的均值相对偏差 均值相对偏差 。

5.The mean relative deviation 均值相对偏差 is often used in finance to evaluate the risk of investment portfolios.

在金融领域，均值相对偏差 均值相对偏差 常用于评估投资组合的风险。

作文

In the field of statistics, understanding the variations in data is crucial for making informed decisions. One important concept that helps in this regard is the mean relative deviation, which provides insights into the dispersion of a dataset relative to its mean value. The mean relative deviation (MRD) is a statistical measure that quantifies how much individual data points deviate from the average, expressed as a percentage of the mean. This allows us to assess the variability of the data in a way that is easy to interpret and compare across different datasets.To calculate the mean relative deviation, one must first determine the mean of the dataset. Once the mean is established, the absolute deviations of each data point from the mean are calculated. These absolute deviations are then averaged, and finally, the result is divided by the mean and multiplied by 100 to express it as a percentage. This process not only highlights the individual variances but also contextualizes them within the framework of the overall dataset.The significance of the mean relative deviation lies in its ability to provide a relative measure of dispersion. Unlike standard deviation, which can sometimes be misleading due to its reliance on squared deviations, the mean relative deviation offers a more intuitive understanding of how spread out the values are in relation to the average. For instance, in a dataset where the mean is significantly larger than the individual values, the standard deviation might suggest low variability, whereas the mean relative deviation would reveal a different story, indicating that the values are indeed widely spread out when viewed as a proportion of the mean.Moreover, the mean relative deviation is particularly useful in fields such as finance, quality control, and research, where understanding the consistency and reliability of data is essential. For example, in financial analysis, investors often look at the mean relative deviation of returns on investment to gauge the risk associated with different assets. A high mean relative deviation indicates that the returns are highly variable, suggesting a higher risk, while a low mean relative deviation implies stability and predictability.In conclusion, the mean relative deviation is an invaluable tool for statisticians and data analysts alike. By providing a clear and concise measure of dispersion relative to the mean, it enables better decision-making and deeper insights into the nature of the data. As we continue to navigate an increasingly data-driven world, mastering concepts like the mean relative deviation will undoubtedly enhance our ability to interpret and utilize information effectively.

在统计学领域，理解数据的变化对于做出明智的决策至关重要。一个有助于这方面的重要概念是均值相对偏差，它提供了关于数据集相对于其均值的分散情况的见解。均值相对偏差（MRD）是一种统计度量，用于量化每个数据点相对于平均值的偏差程度，以百分比的形式表达。这使我们能够以一种易于解释和比较的方式评估数据的变异性。要计算均值相对偏差，首先必须确定数据集的均值。一旦确定了均值，就可以计算每个数据点与均值的绝对偏差。这些绝对偏差然后被平均，最后将结果除以均值并乘以100，以百分比的形式表示。这个过程不仅突出显示了个体的偏差，还将其置于整个数据集的框架内进行上下文化。均值相对偏差的重要性在于它能够提供相对的分散度量。与标准偏差不同，后者有时由于依赖平方偏差而可能产生误导，均值相对偏差提供了对数值在平均值周围的分布情况更直观的理解。例如，在一个均值明显大于个别值的数据集中，标准偏差可能会暗示低变异性，而均值相对偏差则会揭示出不同的故事，表明在相对于均值的比例来看，这些值实际上是广泛分散的。此外，均值相对偏差在金融、质量控制和研究等领域尤其有用，在这些领域中，理解数据的一致性和可靠性至关重要。例如，在财务分析中，投资者通常会查看投资回报的均值相对偏差来衡量不同资产相关的风险。高均值相对偏差表明回报高度可变，暗示着较高的风险，而低均值相对偏差则意味着稳定性和可预测性。总之，均值相对偏差是统计学家和数据分析师不可或缺的工具。通过提供相对于均值的清晰且简明的分散度量，它使得更好的决策和对数据本质的更深刻洞察成为可能。随着我们继续在一个日益数据驱动的世界中航行，掌握像均值相对偏差这样的概念无疑将增强我们有效解释和利用信息的能力。