covariance的音標是[?k?v???re?ns]，中文翻譯為協變性、相關性。速記技巧是利用發音類似記憶法，可以將單詞劃分為幾個部分，如“co-var-ia-nce”，其中“co-var”聽起來很像“相關”的意思。

Covariance這個詞源自拉丁語，意為“共同變化”。它的變化形式包括復數covariances和過去式、過去分詞、現在分詞covariant以及過去分詞covariancy。

相關單詞：

covariance（協方差）：在統計學中，協方差是衡量兩個變量之間相關性的指標。

covariant（共變的）：表示隨著時間或其他變量的變化而變化的。

covariant change（共變變化）：指隨著某個變量的變化，其他變量也相應地變化。

autocovariance（自協方差）：指一個序列中相鄰數據點之間的協方差。

cross-covariance（交叉協方差）：指兩個不同序列之間的協方差。

covariance matrix（協方差矩陣）：表示一組數據中各個變量之間的協方差構成的矩陣。

covariancist（協方差分析者）：進行協方差分析的專業人士。

covariation（共變）：指兩個或多個變量隨著時間的推移而保持一致的變化。

covariation curve（共變曲線）：表示兩個或多個變量之間關系的曲線，其中一方的變化與另一方的變化保持一致。

covariances and covariances（相關性和協方差）：在數據分析中，相關性和協方差是衡量變量之間關系的兩個重要指標。它們可以幫助我們了解數據中的模式和趨勢。

以上這些單詞都與covariance這個詞有著密切的聯系，反映了其在統計學和數據分析中的重要性和應用。

常用短語：

1. correlation coefficient (相關系數)

2. covariance matrix (協方差矩陣)

3. mean covariance (均值協方差)

4. cross-covariance (交叉協方差)

5. sample covariance (樣本協方差)

6. population covariance (總體協方差)

7. covariance analysis (協方差分析)

雙語例句：

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.