mean variation

简明释义

平均偏差

英英释义

例句

1.To assess the quality of the product, we calculated the mean variation 平均变异 in its dimensions.

为了评估产品质量，我们计算了其尺寸的mean variation 平均变异。

2.The study revealed that the mean variation 平均变异 in test scores was significantly reduced after the new teaching methods were implemented.

研究显示，在实施新教学方法后，考试成绩的mean variation 平均变异显著降低。

3.The mean variation 平均变异 in customer feedback scores indicated areas for improvement.

客户反馈评分的mean variation 平均变异显示了需要改进的领域。

4.In statistics, the mean variation 平均变异 is a crucial measure for understanding data dispersion.

在统计学中，mean variation 平均变异是理解数据分散的重要指标。

5.The mean variation 平均变异 of daily temperatures can help meteorologists predict weather patterns.

每日温度的mean variation 平均变异可以帮助气象学家预测天气模式。

作文

In the field of statistics, understanding the concept of mean variation is crucial for data analysis. The mean variation refers to the average of the differences between each data point and the mean of the dataset. This concept helps in assessing the dispersion or spread of a set of values. For instance, if we consider the test scores of a class, calculating the mean variation allows us to see how much individual scores deviate from the average score. A low mean variation indicates that the scores are closely clustered around the mean, while a high mean variation suggests a wider spread of scores, highlighting significant differences among students' performances.To calculate the mean variation, one must first determine the mean of the dataset. This is done by summing all the values and dividing by the number of values. Once the mean is established, the next step involves finding the absolute differences between each data point and the mean. These absolute differences are then summed up and divided by the total number of data points to get the mean variation. This process not only provides insight into the variability of the data but also aids in making informed decisions based on statistical evidence.For example, consider two classes with their respective test scores: Class A has scores of 80, 82, 79, 85, and 81, while Class B has scores of 70, 90, 60, 100, and 50. Calculating the mean for both classes reveals that Class A has a mean score of 81.4 and Class B has a mean score of 74. The mean variation for Class A will be relatively low, indicating that most students performed similarly, whereas Class B will show a high mean variation, reflecting a significant disparity in student performance.Understanding mean variation is not only limited to academic settings; it extends to various fields, including finance, healthcare, and social sciences. For instance, in finance, investors use mean variation to assess the risk associated with an investment portfolio. A portfolio with a high mean variation may indicate higher risk, prompting investors to reconsider their strategies.Moreover, in healthcare, researchers analyze patient data to determine the effectiveness of treatments. By examining the mean variation in patient outcomes, they can identify which treatments yield consistent results and which ones may have unpredictable effects.In conclusion, the concept of mean variation plays a vital role in understanding data variability across different domains. It equips researchers, analysts, and decision-makers with the tools necessary to interpret data accurately and make well-informed choices. As we continue to collect and analyze vast amounts of data in our increasingly data-driven world, mastering the concept of mean variation will remain essential for deriving meaningful insights and fostering advancements in various fields.

在统计学领域，理解均值变异的概念对数据分析至关重要。均值变异是指每个数据点与数据集均值之间差异的平均值。这个概念有助于评估一组值的离散程度或分布情况。例如，如果我们考虑一个班级的考试成绩，计算均值变异可以让我们看到各个分数与平均分的偏差程度。低均值变异表明分数紧密集中在均值附近，而高均值变异则暗示分数分布较广，突显出学生表现之间的显著差异。计算均值变异，首先必须确定数据集的均值。这通过将所有值相加并除以值的数量来完成。一旦均值确定，下一步就是找出每个数据点与均值之间的绝对差异。这些绝对差异随后被相加并除以数据点的总数，从而得到均值变异。这个过程不仅提供了数据变异性的洞察，还帮助基于统计证据做出明智的决策。例如，考虑两个班级及其各自的考试成绩：A班的成绩为80、82、79、85和81，而B班的成绩为70、90、60、100和50。计算两个班级的均值可以发现，A班的均值为81.4，B班的均值为74。A班的均值变异相对较低，表明大多数学生表现相似，而B班则显示出高均值变异，反映出学生表现之间的显著差异。理解均值变异不仅限于学术环境；它扩展到金融、医疗和社会科学等多个领域。例如，在金融领域，投资者使用均值变异来评估投资组合的风险。一个具有高均值变异的投资组合可能表明更高的风险，促使投资者重新考虑他们的策略。此外，在医疗领域，研究人员分析患者数据以确定治疗的有效性。通过检查患者结果的均值变异，他们可以识别哪些治疗方法产生一致的结果，哪些方法可能具有不可预测的效果。总之，均值变异的概念在理解不同领域的数据变异性方面发挥着至关重要的作用。它为研究人员、分析师和决策者提供了准确解读数据和做出明智选择所需的工具。随着我们在日益数据驱动的世界中继续收集和分析大量数据，掌握均值变异的概念将始终是提取有意义见解和促进各个领域进步的关键。