coefficient of variation

简明释义

变异系数

英英释义

例句

1.When comparing two manufacturing processes, the coefficient of variation can highlight which one has less variability.

在比较两个制造过程时，变异系数可以突出哪个过程的变异性更小。

2.In finance, a lower coefficient of variation indicates a more stable investment.

在金融领域，较低的变异系数表示投资更稳定。

3.Researchers often use the coefficient of variation to assess the precision of their measurements.

研究人员通常使用变异系数来评估他们测量的精确度。

4.The coefficient of variation is a useful metric for comparing the relative variability of different datasets.

在比较不同数据集的相对变异性时，变异系数是一个有用的指标。

5.A high coefficient of variation may suggest inconsistent quality in production.

高变异系数可能表明生产质量不一致。

作文

In the realm of statistics, understanding variability is crucial for interpreting data accurately. One important measure that helps in assessing the relative variability of a dataset is the coefficient of variation. The coefficient of variation (变异系数) is defined as the ratio of the standard deviation to the mean, often expressed as a percentage. This metric allows researchers and analysts to compare the degree of variation from one dataset to another, even if the means are drastically different. For instance, consider two different investment portfolios. Portfolio A has an average return of $10,000 with a standard deviation of $2,000, while Portfolio B has an average return of $100,000 with a standard deviation of $20,000. At first glance, one might assume that Portfolio B is less risky due to its higher average return. However, calculating the coefficient of variation reveals a different story. For Portfolio A, the coefficient of variation is 20% (2,000/10,000), while for Portfolio B, it is only 20% (20,000/100,000). This indicates that both portfolios have the same level of risk relative to their respective returns, highlighting the utility of the coefficient of variation in financial analysis. The coefficient of variation is particularly useful in fields such as finance, quality control, and research, where comparing variability is essential. It provides a standardized way to assess risk and performance across different contexts. For example, in quality control, manufacturers may use the coefficient of variation to determine the consistency of product dimensions. If one product has a mean dimension of 50 mm with a standard deviation of 5 mm, and another has a mean of 200 mm with a standard deviation of 10 mm, both products would have a coefficient of variation of 10%. This indicates that the variability in size is consistent across different scales of production. Moreover, the coefficient of variation can also be applied in fields like healthcare, where it can help compare the variability of different treatment outcomes. For instance, if one treatment yields an average recovery time of 30 days with a standard deviation of 6 days, and another treatment has an average recovery time of 60 days with a standard deviation of 12 days, both treatments would again have a coefficient of variation of 20%. This information is vital for healthcare professionals when deciding on the most effective treatment options for patients. In conclusion, the coefficient of variation (变异系数) is a powerful statistical tool that provides insight into the relative variability of datasets. By allowing for comparisons across different units and scales, it plays an essential role in various fields, including finance, quality control, and healthcare. Understanding this concept not only aids in better data interpretation but also enhances decision-making processes based on statistical analysis.