mean variation

简明释义

平均偏差平均自差

英英释义

例句

1.A high mean variation 均值变异 in test scores may suggest inconsistent teaching methods.

测试分数的高均值变异 mean variation 可能表明教学方法不一致。

2.Researchers often report the mean variation 均值变异 to provide context for their findings.

研究人员通常报告均值变异 mean variation 以为他们的发现提供背景。

3.In statistics, the mean variation 均值变异 is used to measure the dispersion of a dataset.

在统计学中，均值变异 mean variation 用于测量数据集的离散程度。

4.The mean variation 均值变异 in temperature readings can indicate climate change over time.

温度读数中的均值变异 mean variation 可以指示气候变化的趋势。

5.When analyzing financial data, understanding the mean variation 均值变异 helps assess risk.

在分析财务数据时，理解均值变异 mean variation 有助于评估风险。

作文

In the realm of statistics, understanding the concept of mean variation is crucial for analyzing data sets effectively. The term mean variation refers to the average of the absolute differences between each data point and the overall mean of the data set. This measure provides insight into how much individual data points deviate from the average, thereby indicating the level of variability within the data. In practical applications, mean variation can be particularly useful in fields such as finance, psychology, and quality control, where understanding fluctuations is essential.Consider a simple example involving the test scores of a group of students. If the scores are 70, 75, 80, 85, and 90, the first step is to calculate the mean score, which is (70 + 75 + 80 + 85 + 90) / 5 = 80. Next, we find the absolute differences between each score and the mean:- |70 - 80| = 10- |75 - 80| = 5- |80 - 80| = 0- |85 - 80| = 5- |90 - 80| = 10Now, we can calculate the mean variation: (10 + 5 + 0 + 5 + 10) / 5 = 6. This result indicates that, on average, the students’ scores deviate from the mean by 6 points.Understanding mean variation allows educators to assess the consistency of student performance. A low mean variation would suggest that most students scored close to the average, while a high mean variation would indicate a wider spread of scores, prompting further investigation into the factors contributing to this discrepancy.In finance, mean variation plays a critical role in risk assessment. Investors often look at the mean variation of stock prices to determine the volatility of their investments. A stock with a high mean variation signifies more risk, as its price fluctuates significantly around the mean. Conversely, a low mean variation indicates stability, making such stocks more attractive to risk-averse investors.Moreover, in the field of psychology, researchers may use mean variation to analyze the variability in responses during experiments. For instance, if a study measures the effectiveness of a new therapy, the mean variation of patient improvement scores can reveal how consistently effective the therapy is across different individuals. A therapy that results in a low mean variation may be deemed more reliable, as it produces similar outcomes for most patients.In quality control, manufacturers utilize mean variation to monitor product consistency. By calculating the mean variation of product dimensions or weights, companies can ensure that their products meet specified standards. A high mean variation could indicate potential issues in the production process, prompting immediate corrective actions to maintain quality.In conclusion, the concept of mean variation is a powerful statistical tool that provides valuable insights across various fields. By measuring the average deviation of data points from the mean, it helps analysts understand variability, assess risks, and make informed decisions. Whether in education, finance, psychology, or manufacturing, mastering the interpretation of mean variation can lead to improved outcomes and enhanced understanding of underlying trends in data. As we continue to navigate a data-driven world, the significance of mean variation will only grow, underscoring the importance of statistical literacy in our everyday lives.

在统计学领域，理解均值变异的概念对于有效分析数据集至关重要。术语均值变异指的是每个数据点与数据集整体均值之间绝对差异的平均值。这个指标提供了个别数据点如何偏离平均值的洞察，从而指示了数据的变异程度。在实际应用中，均值变异在金融、心理学和质量控制等领域尤其有用，因为了解波动情况至关重要。考虑一个简单的例子，涉及一组学生的考试成绩。如果成绩为70、75、80、85和90，第一步是计算平均分，即(70 + 75 + 80 + 85 + 90) / 5 = 80。接下来，我们找到每个分数与均值之间的绝对差：- |70 - 80| = 10- |75 - 80| = 5- |80 - 80| = 0- |85 - 80| = 5- |90 - 80| = 10现在，我们可以计算均值变异：（10 + 5 + 0 + 5 + 10）/ 5 = 6。这个结果表明，平均而言，学生的分数偏离均值6分。理解均值变异使教育工作者能够评估学生表现的一致性。低均值变异表明大多数学生的分数接近平均值，而高均值变异则表示分数差异较大，促使进一步调查导致这种差异的因素。在金融领域，均值变异在风险评估中发挥着关键作用。投资者通常会关注股票价格的均值变异来确定投资的波动性。高均值变异的股票意味着更大的风险，因为其价格在均值周围波动显著。相反，低均值变异表明稳定性，使得这样的股票对厌恶风险的投资者更加吸引。此外，在心理学领域，研究人员可能会使用均值变异来分析实验中的反应变异性。例如，如果一项研究衡量新疗法的有效性，患者改善分数的均值变异可以揭示该疗法在不同个体中的一致性效果。导致低均值变异的疗法可能被认为更可靠，因为它为大多数患者产生类似的结果。在质量控制中，制造商利用均值变异来监测产品的一致性。通过计算产品尺寸或重量的均值变异，公司可以确保其产品符合规定标准。高均值变异可能表明生产过程中存在潜在问题，促使立即采取纠正措施以维持质量。总之，均值变异的概念是一个强大的统计工具，提供了各个领域的有价值的见解。通过测量数据点偏离均值的平均值，它帮助分析师理解变异性、评估风险并做出明智的决策。无论是在教育、金融、心理学还是制造业，掌握均值变异的解释都可以带来更好的结果，提高对数据中潜在趋势的理解。随着我们继续在数据驱动的世界中航行，均值变异的重要性只会增加，强调了统计素养在我们日常生活中的重要性。