covariation的音標(biāo)是[?k?v???re??n]，翻譯為“共變；相關(guān)性”。

速記技巧：可以記為“co-vary（共變）”。

Covariation這個(gè)詞的英文詞源可以追溯到拉丁語(yǔ)中的“co”表示共同或一起，“variatio”表示變化或變異。這個(gè)詞在英語(yǔ)中用來(lái)表示兩個(gè)變量之間隨著時(shí)間或條件的變化而變化的關(guān)系。

變化形式：名詞形式為covariance，動(dòng)詞形式為covariate。

相關(guān)單詞：

1. Correlation：這個(gè)詞也是用來(lái)描述兩個(gè)變量之間的關(guān)系，但是它更強(qiáng)調(diào)的是數(shù)值上的關(guān)系，即它們之間的線性關(guān)系。

2. Regression：回歸這個(gè)詞用來(lái)描述當(dāng)兩個(gè)或更多個(gè)變量之間存在相關(guān)性時(shí)，我們?cè)噲D找到一種數(shù)學(xué)模型來(lái)描述它們之間的關(guān)系的過(guò)程。

3. Coefficient：在回歸分析中，我們經(jīng)常使用一個(gè)系數(shù)來(lái)描述兩個(gè)變量之間的線性關(guān)系，如R-squared，Pearsons coefficient等。

4. Covariate：如上文所述，這是一個(gè)名詞形式，用來(lái)表示一個(gè)變量，它在分析兩個(gè)或更多個(gè)變量之間的關(guān)系時(shí)，被視為一個(gè)可能影響結(jié)果的因素。

5. Covariance：這是動(dòng)詞covariate的名詞形式，用來(lái)表示兩個(gè)變量之間的協(xié)方差，這是衡量?jī)蓚€(gè)變量相關(guān)程度的一個(gè)統(tǒng)計(jì)量。

6. Conformity：這個(gè)詞在某些語(yǔ)境下也可以用來(lái)描述兩個(gè)變量之間的關(guān)系，但它更強(qiáng)調(diào)的是它們之間的相似性或一致性。

7. Mutualism：這個(gè)詞用來(lái)描述兩個(gè)或更多個(gè)因素之間相互促進(jìn)的關(guān)系。

8. Dependence：這個(gè)詞用來(lái)描述兩個(gè)或更多個(gè)因素之間的一種關(guān)系，其中一個(gè)因素的變化會(huì)影響另一個(gè)因素的變化。

9. Alliance：聯(lián)盟這個(gè)詞也可以用來(lái)描述兩個(gè)或更多個(gè)因素之間的一種關(guān)系，它們共同行動(dòng)以實(shí)現(xiàn)共同的目標(biāo)。

10. Coherence：一致性這個(gè)詞也可以用來(lái)描述兩個(gè)或更多個(gè)因素之間的某種關(guān)系，它們?cè)谀撤N程度上是相互關(guān)聯(lián)的，形成一個(gè)整體。

常用短語(yǔ)：

1. correlation coefficient

2. covariation rate

3. covariation analysis

4. covariation pattern

5. covariation relationship

6. covariation matrix

7. covariation factor

例句：

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

2. Over time, we observed a strong covariation between the price of oil and the exchange rate.

3. The covariation rate between the two countries" economic indicators has increased significantly over the past decade.

4. The covariation pattern between these two variables is complex and requires further investigation.

5. The covariation relationship between climate change and biodiversity is becoming increasingly apparent.

6. The covariation matrix shows that these variables are strongly correlated with each other.

7. Covariation factors such as technology and demographics are influencing the way businesses operate.

英文小作文：

The concept of covariation is crucial in understanding the interrelationships between variables in a system. When two or more variables are found to be consistently related over time or in different contexts, it indicates that they are influenced by common factors or are subject to similar forces. This concept is particularly important in fields such as ecology, where the relationship between species and their environment is studied, and economics, where the impact of factors such as technology and demographics on economic performance is investigated.

In many real-world systems, covariation can be used to identify patterns and trends that would otherwise be difficult to detect using other methods. For example, in the financial industry, the analysis of covariance can help identify trends in asset prices that are influenced by common factors such as interest rates or economic conditions. Similarly, in the field of medicine, the analysis of covariance can help identify patterns in disease incidence that are influenced by common environmental factors or shared risk factors among patients.

However, it is important to note that covariation does not necessarily imply causality. While covariation can indicate relationships between variables, it does not necessarily prove that one variable causes changes in another. Therefore, it is essential to use other methods, such as experiments or observations, to establish causal relationships between variables. Nevertheless, the concept of covariation remains an essential tool in understanding the interrelationships between variables in a system and can provide valuable insights into how systems function and evolve.