mean variation

简明释义

平均自差

英英释义

例句

1.The study analyzed the mean variation 均值变异 in test scores across different demographics.

该研究分析了不同人群中测试分数的均值变异。

2.Researchers reported a high mean variation 均值变异 in the effectiveness of different medications.

研究人员报告了不同药物效果的均值变异很高。

3.To better understand the data, we calculated the mean variation 均值变异 in customer satisfaction ratings.

为了更好地理解数据，我们计算了顾客满意度评分的均值变异。

4.In finance, the mean variation 均值变异 of stock prices can indicate market volatility.

在金融领域，股票价格的均值变异可以指示市场波动性。

5.The mean variation 均值变异 of temperatures over the year showed significant climate change.

全年温度的均值变异显示出显著的气候变化。

作文

In the field of statistics, understanding data is crucial for making informed decisions. One important concept that often comes up is mean variation, which refers to the average of the squared differences from the mean. This statistical measure helps us understand how much individual data points differ from the overall average. When analyzing a dataset, it is essential to consider the mean variation because it provides insight into the distribution and variability of the data. For instance, imagine a teacher who has recorded the test scores of her students. If the scores are tightly clustered around the average score, the mean variation will be low, indicating that most students performed similarly. Conversely, if the scores are spread out widely, the mean variation will be high, suggesting a significant difference in student performance. This information can guide the teacher in identifying which students may need additional support or which topics might require further review. Moreover, mean variation plays a vital role in various fields, including finance, engineering, and social sciences. In finance, investors analyze the mean variation of stock prices to assess risk. A higher mean variation indicates greater volatility, which may deter risk-averse investors. On the other hand, a lower mean variation suggests stability, making the investment more attractive. Understanding this concept allows investors to make better choices based on their risk tolerance. In engineering, mean variation can be used to evaluate the quality of products. For example, if a manufacturer produces screws, the mean variation in the length of the screws can indicate the precision of the manufacturing process. A low mean variation means that the screws are consistently produced to the desired length, ensuring they fit properly in their intended applications. High mean variation, however, could lead to issues with product compatibility and customer satisfaction. Additionally, researchers in social sciences utilize mean variation to analyze survey data. By calculating the mean variation of responses, they can determine how diverse opinions are within a population. This can be particularly useful in understanding public sentiment on various issues, such as healthcare or education policies. A high mean variation in responses might suggest that there is considerable disagreement among respondents, which could influence policymakers' decisions. In conclusion, the concept of mean variation is fundamental in statistics and has wide-ranging applications across different fields. By measuring the average of the squared differences from the mean, we gain valuable insights into the variability and distribution of data. Whether it's in education, finance, engineering, or social sciences, understanding mean variation helps us interpret data more effectively and make better-informed decisions. As we continue to navigate an increasingly data-driven world, mastering concepts like mean variation will be essential for success in various domains.

在统计学领域，理解数据对于做出明智的决策至关重要。一个经常出现的重要概念是均方差，它指的是与均值的平方差的平均值。这一统计测量帮助我们理解个别数据点与整体平均值之间的差异。在分析数据集时，考虑均方差是至关重要的，因为它提供了关于数据分布和变异性的洞察。例如，想象一位老师记录了她学生的考试成绩。如果成绩紧密集中在平均分附近，则均方差将很低，表明大多数学生的表现相似。相反，如果成绩分散得很广，均方差将很高，这表明学生表现之间存在显著差异。这些信息可以指导老师识别哪些学生可能需要额外支持，或者哪些主题可能需要进一步复习。此外，均方差在金融、工程和社会科学等多个领域中发挥着重要作用。在金融领域，投资者分析股票价格的均方差以评估风险。较高的均方差表示更大的波动性，这可能会使风险厌恶型投资者却步。另一方面，较低的均方差则表明稳定性，使投资更具吸引力。理解这一概念使投资者能够根据他们的风险承受能力做出更好的选择。在工程领域，均方差可用于评估产品质量。例如，如果一家制造商生产螺钉，螺钉长度的均方差可以指示制造过程的精确度。较低的均方差意味着螺钉始终按照所需长度生产，确保它们在预期应用中的适配性。然而，高均方差可能导致产品兼容性和客户满意度的问题。此外，社会科学研究人员利用均方差分析调查数据。通过计算响应的均方差，他们可以确定人口中意见的多样性。这在理解公众对各种问题（例如医疗保健或教育政策）的情感时尤其有用。响应中较高的均方差可能表明受访者之间存在相当大的分歧，这可能影响政策制定者的决策。总之，均方差这一概念在统计学中是基础，并在不同领域有广泛的应用。通过测量与均值的平方差的平均值，我们获得了关于数据变异性和分布的宝贵见解。无论是在教育、金融、工程还是社会科学中，理解均方差都有助于我们更有效地解释数据并做出更明智的决策。随着我们继续在一个日益依赖数据的世界中航行，掌握像均方差这样的概念将对各个领域的成功至关重要。