covariance

简明释义

英[kəʊˈveərɪəns]美[koˈverɪəns]

n. [数] 协方差；共分散

英英释义

单词用法

同义词

反义词

例句

1.Thereinto, for the spectral decomposition estimate of the covariance matrix , we can gain the risk functions under some losses.

其中，对于观测向量协方差阵的谱分解估计，我们很容易得到它在一些损失下的风险函数。

2.Method: Dummy variable is used for analysis of covariance and analysis of variance.

方法：在协方差分析与方差分析中使用哑变量。

3.An improved measuring method, the so-called eddy covariance method, recently became available.

最近开始应用的是一种改进的测量方法，所谓的涡度相关法。

4.The distinction between covariance and contravariance is important for computations with tensors, which often have mixed variance.

协变和逆变的区别对于张量的计算尤为重要，这往往会出现混合方差。

5.The computation formulas for the prediction error covariance matrix between any two subsystems are given.

同时给出了任两个传感器之间的预报误差协方差阵的计算公式。

6.If you get a lot of negative products, that makes the covariance negative.

很多个负值的结果相加，就会使协方差是一个负值。

7.In statistics, the covariance 协方差 between two variables indicates the direction of their linear relationship.

在统计学中，两个变量之间的covariance 协方差 表示它们线性关系的方向。

8.In finance, the covariance 协方差 between the returns of two stocks can help investors understand their risk profile.

在金融中，两只股票收益之间的covariance 协方差 可以帮助投资者了解他们的风险特征。

9.The formula for calculating covariance 协方差 involves the mean of each variable and their deviations from that mean.

计算covariance 协方差 的公式涉及每个变量的均值及其偏离该均值的差异。

10.A positive covariance 协方差 suggests that as one variable increases, the other tends to increase as well.

正的covariance 协方差 表明当一个变量增加时，另一个变量也倾向于增加。

11.When analyzing data, a high covariance 协方差 value may indicate a strong relationship between the variables.

在分析数据时，高covariance 协方差 值可能表明变量之间存在强关系。

作文

Covariance is a statistical measure that indicates the extent to which two random variables change together. In simpler terms, it helps us understand how the value of one variable is related to the value of another variable. For example, in finance, if we want to analyze the relationship between the returns of two different stocks, we can use covariance to see whether they tend to increase or decrease together. If the covariance is positive, it implies that when one stock's return increases, the other stock's return also tends to increase. Conversely, if the covariance is negative, it indicates that when one stock's return increases, the other stock's return tends to decrease. This relationship can be crucial for investors when diversifying their portfolios. By understanding the covariance (协方差) between different assets, investors can make informed decisions about how to allocate their resources effectively.In addition to finance, covariance (协方差) is widely used in various fields such as economics, biology, and social sciences. For instance, in biology, researchers may examine the covariance (协方差) between the height and weight of individuals within a population. A positive covariance (协方差) would suggest that taller individuals tend to weigh more, while a negative covariance (协方差) would imply that taller individuals weigh less. Such analysis can help scientists understand the relationships between different biological traits and can lead to further research and discovery.Moreover, covariance (协方差) plays a significant role in machine learning and data analysis. When building predictive models, understanding the covariance (协方差) between features can help in feature selection and engineering. For example, if two features have high positive covariance (协方差), it might indicate redundancy, suggesting that one of the features could be removed without losing much information. This simplification can lead to more efficient models and quicker computations.It is essential to note that while covariance (协方差) provides valuable information about the direction of the relationship between two variables, it does not indicate the strength of that relationship. To measure the strength of the relationship, we often look at the correlation coefficient, which standardizes the covariance (协方差) by the standard deviations of the variables involved. This allows us to compare the relationships between different pairs of variables more effectively.In conclusion, covariance (协方差) is a fundamental concept in statistics that helps us understand the relationship between two variables. Whether in finance, biology, or data science, recognizing the significance of covariance (协方差) can lead to better decision-making and deeper insights into the data we analyze. As we continue to explore the vast amounts of data available today, grasping the concept of covariance (协方差) will undoubtedly remain a critical skill for researchers, analysts, and investors alike.

协方差是一个统计量，它表示两个随机变量共同变化的程度。简单来说，它帮助我们理解一个变量的值与另一个变量的值之间的关系。例如，在金融领域，如果我们想分析两只不同股票的收益之间的关系，我们可以使用协方差来观察它们是否倾向于一起增加或减少。如果协方差为正，则表示当一只股票的收益增加时，另一只股票的收益也倾向于增加。相反，如果协方差为负，则表明当一只股票的收益增加时，另一只股票的收益倾向于减少。这种关系对于投资者在多样化其投资组合时至关重要。通过理解不同资产之间的协方差（covariance），投资者可以做出明智的决策，有效地分配他们的资源。除了金融领域，协方差（covariance）在经济学、生物学和社会科学等多个领域广泛应用。例如，在生物学中，研究人员可能会检查个体在一个种群中的身高和体重之间的协方差（covariance）。正的协方差（covariance）将表明较高的个体往往体重较重，而负的协方差（covariance）则意味着较高的个体体重较轻。这种分析可以帮助科学家理解不同生物特征之间的关系，并可能导致进一步的研究和发现。此外，协方差（covariance）在机器学习和数据分析中也扮演着重要角色。在构建预测模型时，理解特征之间的协方差（covariance）可以帮助进行特征选择和工程。例如，如果两个特征具有较高的正协方差（covariance），这可能表明冗余，暗示可以去除其中一个特征而不会失去太多信息。这种简化可以导致更高效的模型和更快的计算。需要注意的是，虽然协方差（covariance）提供了关于两个变量之间关系方向的重要信息，但它并不指示该关系的强度。为了测量关系的强度，我们通常查看相关系数，它通过涉及变量的标准差对协方差（covariance）进行标准化。这使我们能够更有效地比较不同变量对之间的关系。总之，协方差（covariance）是统计学中的一个基本概念，有助于我们理解两个变量之间的关系。无论是在金融、生物学还是数据科学中，认识到协方差（covariance）的重要性都能带来更好的决策和更深入的数据洞察。随着我们继续探索当今可用的大量数据，掌握协方差（covariance）的概念无疑将继续成为研究人员、分析师和投资者的重要技能。