covariance

covariance的音标是[ˌkɒvɪəˈreɪns]，中文翻译为协变性、相关性。速记技巧是利用发音类似记忆法，可以将单词划分为几个部分，如“co-var-ia-nce”，其中“co-var”听起来很像“相关”的意思。

Covariance这个词源自拉丁语，意为“共同变化”。它的变化形式包括复数covariances和过去式、过去分词、现在分词covariant以及过去分词covariancy。

相关单词：

covariance（协方差）：在统计学中，协方差是衡量两个变量之间相关性的指标。

covariant（共变的）：表示随着时间或其他变量的变化而变化的。

covariant change（共变变化）：指随着某个变量的变化，其他变量也相应地变化。

autocovariance（自协方差）：指一个序列中相邻数据点之间的协方差。

cross-covariance（交叉协方差）：指两个不同序列之间的协方差。

covariance matrix（协方差矩阵）：表示一组数据中各个变量之间的协方差构成的矩阵。

covariancist（协方差分析者）：进行协方差分析的专业人士。

covariation（共变）：指两个或多个变量随着时间的推移而保持一致的变化。

covariation curve（共变曲线）：表示两个或多个变量之间关系的曲线，其中一方的变化与另一方的变化保持一致。

covariances and covariances（相关性和协方差）：在数据分析中，相关性和协方差是衡量变量之间关系的两个重要指标。它们可以帮助我们了解数据中的模式和趋势。

以上这些单词都与covariance这个词有着密切的联系，反映了其在统计学和数据分析中的重要性和应用。

常用短语：

1. correlation coefficient (相关系数)

2. covariance matrix (协方差矩阵)

3. mean covariance (均值协方差)

4. cross-covariance (交叉协方差)

5. sample covariance (样本协方差)

6. population covariance (总体协方差)

7. covariance analysis (协方差分析)

双语例句：

1. The correlation coefficient between the two variables is 0.9, indicating a strong positive correlation.

2. The covariance matrix for the dataset shows that variables x and y are negatively correlated.

3. The mean covariance between product A and B is 0.2, indicating a moderate level of correlation.

4. Cross-covariance analysis suggests that changes in temperature and precipitation are related.

5. The sample covariance gives us an estimate of the true covariance between variables.

6. Population covariance analysis shows that the true mean of the data is different from the sample mean.

7. Covariance analysis can be used to investigate the relationship between two or more variables.

英文小作文：

Covariance is a measure of the relationship between two variables, and it can be used to understand how changes in one variable are related to changes in another variable. In this essay, we will explore the concept of covariance through a real-world example.

Consider a study that tracks the sales of two products - product A and product B - over a period of time. We can calculate the sample covariance to estimate the true covariance between these two products, and this can give us insights into how their sales are related. For example, if product A"s sales increase, we might expect to see an increase in product B"s sales as well, indicating a positive correlation between the two products. On the other hand, if product B"s sales increase but product A"s sales do not, this could indicate a negative correlation between the two products. Understanding these relationships can help us better understand our data and make better decisions about how to manage our products and their sales.