mean dispersion

简明释义

平均分散

英英释义

例句

1.In finance, understanding the mean dispersion 均值离散度 of asset returns can help in risk assessment.

在金融领域，理解资产回报的均值离散度可以帮助进行风险评估。

2.The mean dispersion 均值离散度 of temperatures over the year was calculated to understand climate variability.

为了理解气候变化，计算了全年温度的均值离散度。

3.Researchers found that the mean dispersion 均值离散度 of patient recovery times varied significantly among different treatments.

研究人员发现，不同治疗方法下患者恢复时间的均值离散度差异显著。

4.The report highlighted the mean dispersion 均值离散度 of sales figures across various regions.

报告强调了各地区销售数据的均值离散度。

5.The study analyzed the mean dispersion 均值离散度 of test scores across different schools.

这项研究分析了不同学校测试分数的均值离散度。

作文

In the field of statistics, understanding data is crucial for making informed decisions. One important concept that plays a significant role in this understanding is mean dispersion. This term refers to the way data values spread out around the average or mean value. Essentially, it gives us insight into how much variability exists in a dataset. For instance, if we consider the test scores of a class of students, the mean dispersion would help us determine whether most students scored close to the average score or if there were some who scored significantly higher or lower than the mean.The calculation of mean dispersion can be done using various statistical measures, such as the range, variance, and standard deviation. Each of these measures provides different insights into the data's spread. The range gives us a simple measure of the difference between the highest and lowest values, while variance and standard deviation provide a more nuanced view by considering how each data point deviates from the mean.For example, let’s say we have the following test scores: 70, 75, 80, 85, and 90. The mean score is 80. The range, in this case, would be 20 (90 - 70), indicating a certain level of mean dispersion. However, if we examine the variance and standard deviation, we would find that these measures give us a clearer picture of how concentrated or spread out the scores are around the mean.Understanding mean dispersion is not only important in academic settings but also in various fields such as finance, healthcare, and social sciences. For instance, in finance, investors look at the mean dispersion of stock returns to assess the risk associated with an investment. A high mean dispersion indicates more volatility, which could lead to higher risks and potential rewards. Conversely, a low mean dispersion suggests stability, which might appeal to more risk-averse investors.In healthcare, researchers often analyze mean dispersion when studying patient outcomes. By examining the variability in treatment responses, they can identify which treatments work best for different populations. This understanding can lead to more personalized medicine, where treatments are tailored based on the mean dispersion of responses observed in clinical trials.Moreover, in social sciences, understanding the mean dispersion of survey responses can provide valuable insights into public opinion. For example, if a survey on job satisfaction shows a high mean dispersion, it may indicate that while some employees are very satisfied, others are extremely dissatisfied, highlighting the need for organizational changes.In conclusion, the concept of mean dispersion is fundamental to data analysis across various disciplines. It helps us understand the variability and distribution of data points around the mean, providing essential insights that can guide decision-making processes. Whether in education, finance, healthcare, or social research, grasping the implications of mean dispersion allows stakeholders to make better-informed choices based on the underlying patterns in their data.

在统计学领域，理解数据对于做出明智的决策至关重要。一个在这种理解中起重要作用的概念是均值离散度。这个术语指的是数据值围绕平均值或均值的分布方式。基本上，它让我们洞察数据集中存在多少变异性。例如，如果我们考虑一班学生的考试成绩，均值离散度将帮助我们确定大多数学生的分数是否接近平均分，或者是否有一些学生的分数显著高于或低于均值。均值离散度的计算可以通过各种统计测量来完成，例如范围、方差和标准差。这些测量提供了关于数据分布的不同见解。范围给我们提供了最高值和最低值之间差异的简单测量，而方差和标准差则通过考虑每个数据点与均值的偏差来提供更细致的视角。例如，假设我们有以下考试成绩：70、75、80、85和90。均分为80。在这种情况下，范围将是20（90 - 70），表明某种程度的均值离散度。然而，如果我们检查方差和标准差，我们会发现这些测量为我们提供了分数围绕均值的集中或分散程度的更清晰的图景。理解均值离散度不仅在学术环境中重要，在金融、医疗保健和社会科学等多个领域也同样重要。例如，在金融领域，投资者查看股票收益的均值离散度来评估投资相关的风险。高均值离散度表示更多的波动性，这可能导致更高的风险和潜在的回报。相反，低均值离散度则暗示稳定性，这可能吸引更厌恶风险的投资者。在医疗保健中，研究人员在研究患者结果时经常分析均值离散度。通过检查治疗反应的变异性，他们可以识别哪些治疗对不同人群最有效。这种理解可以导致更个性化的医学，其中治疗根据临床试验中观察到的反应的均值离散度进行调整。此外，在社会科学中，理解调查回应的均值离散度可以提供有关公众意见的宝贵见解。例如，如果一项关于工作满意度的调查显示出高均值离散度，这可能表明尽管一些员工非常满意，但其他员工极其不满意，突显了组织变革的必要性。总之，均值离散度的概念是各个学科数据分析的基础。它帮助我们理解数据点围绕均值的变异性和分布，为指导决策过程提供了重要的见解。无论是在教育、金融、医疗还是社会研究中，掌握均值离散度的含义使利益相关者能够基于数据中的潜在模式做出更明智的选择。