mean absolute deviation

简明释义

绝对平均离差,绝对平均偏差

英英释义

例句

1.Teachers can use the mean absolute deviation 平均绝对偏差 to evaluate the consistency of student test scores.

教师可以使用mean absolute deviation 平均绝对偏差来评估学生考试成绩的一致性。

2.The mean absolute deviation 平均绝对偏差 is particularly useful when comparing two different datasets.

当比较两个不同的数据集时，mean absolute deviation 平均绝对偏差特别有用。

3.The mean absolute deviation 平均绝对偏差 of the dataset helps us understand the variability of the data points.

数据集的mean absolute deviation 平均绝对偏差帮助我们理解数据点的变异性。

4.In finance, the mean absolute deviation 平均绝对偏差 can be used to assess the risk of an investment portfolio.

在金融中，mean absolute deviation 平均绝对偏差可以用来评估投资组合的风险。

5.To measure the accuracy of predictions, we often calculate the mean absolute deviation 平均绝对偏差 between predicted and actual values.

为了测量预测的准确性，我们通常计算预测值和实际值之间的mean absolute deviation 平均绝对偏差。

作文

In the realm of statistics, understanding data variability is crucial for making informed decisions. One of the key concepts that help in measuring this variability is the mean absolute deviation. The mean absolute deviation (MAD) is a measure of dispersion that quantifies the average distance between each data point and the mean of the dataset. By focusing on absolute values, the mean absolute deviation avoids the pitfalls of negative values that can skew other measures of variability, such as variance and standard deviation.To illustrate how the mean absolute deviation works, let’s consider a simple dataset: {4, 8, 6, 5, 3}. First, we calculate the mean of this dataset. The mean is obtained by adding all the values together and dividing by the number of values. In this case, the mean is (4 + 8 + 6 + 5 + 3) / 5 = 5.2.Next, we find the absolute deviations of each data point from the mean. This involves subtracting the mean from each data point and taking the absolute value of the result:- |4 - 5.2| = 1.2- |8 - 5.2| = 2.8- |6 - 5.2| = 0.8- |5 - 5.2| = 0.2- |3 - 5.2| = 2.2Now, we have the absolute deviations: {1.2, 2.8, 0.8, 0.2, 2.2}. The next step is to calculate the mean absolute deviation by finding the mean of these absolute deviations. We add them together and divide by the number of data points: (1.2 + 2.8 + 0.8 + 0.2 + 2.2) / 5 = 1.44. Therefore, the mean absolute deviation of our dataset is 1.44.The mean absolute deviation is particularly useful because it provides insight into the spread of the data without being overly affected by outliers. For instance, if one of the numbers in our dataset were significantly larger or smaller than the others, the mean absolute deviation would still give us a reliable measure of variability. This is why many analysts prefer using the mean absolute deviation when dealing with datasets that may contain extreme values.Moreover, the mean absolute deviation can be applied in various fields such as finance, quality control, and social sciences. In finance, investors may use the mean absolute deviation to assess the risk associated with an investment portfolio by examining how much individual returns deviate from the average return. In quality control, manufacturers may utilize the mean absolute deviation to monitor product consistency and ensure that they meet predefined standards.In conclusion, the mean absolute deviation is a valuable statistical tool that enhances our understanding of data variability. By providing a clear and straightforward measure of dispersion, it allows researchers and analysts to make better-informed decisions based on the underlying patterns within their data. Whether in academia or industry, mastering the concept of mean absolute deviation is essential for anyone looking to analyze data effectively.

在统计学领域，理解数据的变异性对于做出明智的决策至关重要。帮助衡量这种变异性的关键概念之一是均值绝对偏差。均值绝对偏差（MAD）是一种分散度的度量，它量化了每个数据点与数据集均值之间的平均距离。通过关注绝对值，均值绝对偏差避免了负值可能造成的偏差，这些负值会影响其他变异性度量，例如方差和标准差。为了说明均值绝对偏差的工作原理，让我们考虑一个简单的数据集：{4, 8, 6, 5, 3}。首先，我们计算这个数据集的均值。均值是通过将所有值相加并除以值的数量来获得的。在这种情况下，均值为（4 + 8 + 6 + 5 + 3）/ 5 = 5.2。接下来，我们找到每个数据点与均值的绝对偏差。这涉及到从每个数据点中减去均值并取结果的绝对值：- |4 - 5.2| = 1.2- |8 - 5.2| = 2.8- |6 - 5.2| = 0.8- |5 - 5.2| = 0.2- |3 - 5.2| = 2.2现在，我们得到了绝对偏差：{1.2, 2.8, 0.8, 0.2, 2.2}。下一步是通过对这些绝对偏差求均值来计算均值绝对偏差。我们将它们加在一起并除以数据点的数量：（1.2 + 2.8 + 0.8 + 0.2 + 2.2）/ 5 = 1.44。因此，我们的数据集的均值绝对偏差为1.44。均值绝对偏差特别有用，因为它提供了数据分布的洞察，而不会受到极端值的过度影响。例如，如果我们的数据集中有一个数字显著大于或小于其他数字，均值绝对偏差仍然会给我们一个可靠的变异性度量。这就是为什么许多分析师在处理可能包含极端值的数据集时更喜欢使用均值绝对偏差。此外，均值绝对偏差可以应用于金融、质量控制和社会科学等多个领域。在金融领域，投资者可能会使用均值绝对偏差来评估与投资组合相关的风险，通过检查各个回报与平均回报之间的偏差。在质量控制中，制造商可能利用均值绝对偏差来监测产品的一致性，并确保它们符合预定标准。总之，均值绝对偏差是一个有价值的统计工具，增强了我们对数据变异性的理解。通过提供清晰而直接的分散度量，它使研究人员和分析师能够根据数据中的潜在模式做出更好的决策。在学术界或行业中，掌握均值绝对偏差的概念对于任何希望有效分析数据的人来说都是必不可少的。