statistical variation or dispersion

简明释义

统计变异,统计离差

英英释义

例句

1.In our recent study, we observed significant statistical variation or dispersion 统计变异或离散 in the test scores of students from different schools.

在我们最近的研究中，我们观察到来自不同学校的学生测试分数有显著的统计变异或离散。

2.The statistical variation or dispersion 统计变异或离散 of temperatures over the past decade shows a clear trend of climate change.

过去十年温度的统计变异或离散显示出气候变化的明显趋势。

3.Understanding the statistical variation or dispersion 统计变异或离散 helps us identify outliers in our dataset.

理解统计变异或离散有助于我们识别数据集中异常值。

4.The statistical variation or dispersion 统计变异或离散 in the sales data indicates that some products are performing significantly better than others.

销售数据中的统计变异或离散表明某些产品的表现显著优于其他产品。

5.By analyzing the statistical variation or dispersion 统计变异或离散 in customer feedback, we can improve our services.

通过分析客户反馈中的统计变异或离散，我们可以改善我们的服务。

作文

In the field of statistics, understanding the concept of statistical variation or dispersion is crucial for interpreting data accurately. Statistical variation or dispersion refers to the extent to which data points in a set differ from each other and from the mean (average) of the set. This concept is essential because it provides insights into the reliability and consistency of the data being analyzed. A dataset with low statistical variation or dispersion indicates that the values are closely clustered around the mean, suggesting a high level of consistency. Conversely, high statistical variation or dispersion signifies that the values are spread out over a wider range, reflecting greater diversity among the data points.To illustrate this, consider two different classes of students taking the same exam. In Class A, most students score between 85 and 90, while in Class B, scores range from 60 to 100. Although both classes may have the same average score, the statistical variation or dispersion in Class A is much lower than in Class B. This difference is significant because it tells us more about the performance consistency of each class. Teachers might prefer a class with low statistical variation or dispersion, as it indicates that most students understand the material well.Moreover, statistical variation or dispersion plays a vital role in various fields beyond education. In finance, for example, investors analyze the statistical variation or dispersion of stock returns to assess risk. A stock with high statistical variation or dispersion may be considered more volatile and risky, while one with low statistical variation or dispersion is viewed as more stable. This understanding helps investors make informed decisions about where to allocate their resources.In scientific research, statistical variation or dispersion is equally important. Researchers often conduct experiments to test hypotheses, and they must account for statistical variation or dispersion to ensure their results are valid. If a study shows a significant effect but has high statistical variation or dispersion, the findings may not be reliable. Therefore, controlling for statistical variation or dispersion through careful experimental design is essential for obtaining credible results.Ultimately, grasping the concept of statistical variation or dispersion enables individuals to make better decisions based on data. Whether in education, finance, or scientific research, recognizing how data points differ from one another and from the mean can lead to more accurate conclusions and effective strategies. As we navigate an increasingly data-driven world, the ability to interpret statistical variation or dispersion will become even more critical. By honing our skills in analyzing this aspect of data, we can enhance our understanding of complex issues and contribute to more informed discussions and decisions in various domains.In conclusion, statistical variation or dispersion is a foundational concept in statistics that affects many areas of life. Understanding this concept allows for deeper insights into data sets, leading to better decision-making in education, finance, and research. As we continue to encounter vast amounts of data in our daily lives, mastering the analysis of statistical variation or dispersion will be an invaluable skill for anyone looking to thrive in today's data-centric society.

在统计学领域，理解统计变异或离散度的概念对准确解读数据至关重要。统计变异或离散度是指数据集中数据点彼此之间以及与该集均值（平均值）之间的差异程度。这个概念是非常重要的，因为它提供了关于所分析数据的可靠性和一致性的见解。低统计变异或离散度的数据集表明，数值紧密聚集在均值附近，意味着高度一致性。相反，高统计变异或离散度则表明数值在更广泛的范围内分散，反映出数据点之间的多样性。为了说明这一点，考虑两个不同班级的学生参加同一考试。在A班，大多数学生的分数在85到90之间，而在B班，分数范围从60到100。尽管这两个班级的平均分可能相同，但A班的统计变异或离散度远低于B班。这种差异是显著的，因为它能更好地告诉我们每个班级的表现一致性。教师可能更喜欢统计变异或离散度低的班级，因为这表明大多数学生很好地理解了材料。此外，统计变异或离散度在教育以外的多个领域也起着至关重要的作用。例如，在金融领域，投资者分析股票收益的统计变异或离散度以评估风险。高统计变异或离散度的股票可能被视为更具波动性和风险，而低统计变异或离散度的股票则被视为更稳定。这种理解帮助投资者做出明智的资源分配决策。在科学研究中，统计变异或离散度同样重要。研究人员通常进行实验以测试假设，他们必须考虑统计变异或离散度以确保结果的有效性。如果一项研究显示出显著的效果，但具有高统计变异或离散度，那么这些发现可能不可靠。因此，通过仔细的实验设计来控制统计变异或离散度对于获得可信的结果至关重要。最终，掌握统计变异或离散度的概念使个人能够基于数据做出更好的决策。无论是在教育、金融还是科学研究中，认识到数据点之间以及与均值之间的差异可以导致更准确的结论和有效的策略。随着我们进入一个日益数据驱动的世界，解释统计变异或离散度的能力将变得更加关键。通过提高我们分析这一数据方面的技能，我们可以增强对复杂问题的理解，并在各个领域为更明智的讨论和决策做出贡献。总之，统计变异或离散度是统计学中的一个基础概念，影响着生活的许多领域。理解这一概念使我们能够深入洞察数据集，从而在教育、金融和研究中做出更好的决策。随着我们继续在日常生活中遇到大量数据，掌握对统计变异或离散度的分析将成为任何希望在当今数据中心社会中蓬勃发展的人的宝贵技能。