covariation

covariation的音标是[ˌkɒvɪəˈreɪʃn]，翻译为“共变；相关性”。

速记技巧：可以记为“co-vary（共变）”。

Covariation这个词的英文词源可以追溯到拉丁语中的“co”表示共同或一起，“variatio”表示变化或变异。这个词在英语中用来表示两个变量之间随着时间或条件的变化而变化的关系。

变化形式：名词形式为covariance，动词形式为covariate。

相关单词：

1. Correlation：这个词也是用来描述两个变量之间的关系，但是它更强调的是数值上的关系，即它们之间的线性关系。

2. Regression：回归这个词用来描述当两个或更多个变量之间存在相关性时，我们试图找到一种数学模型来描述它们之间的关系的过程。

3. Coefficient：在回归分析中，我们经常使用一个系数来描述两个变量之间的线性关系，如R-squared，Pearsons coefficient等。

4. Covariate：如上文所述，这是一个名词形式，用来表示一个变量，它在分析两个或更多个变量之间的关系时，被视为一个可能影响结果的因素。

5. Covariance：这是动词covariate的名词形式，用来表示两个变量之间的协方差，这是衡量两个变量相关程度的一个统计量。

6. Conformity：这个词在某些语境下也可以用来描述两个变量之间的关系，但它更强调的是它们之间的相似性或一致性。

7. Mutualism：这个词用来描述两个或更多个因素之间相互促进的关系。

8. Dependence：这个词用来描述两个或更多个因素之间的一种关系，其中一个因素的变化会影响另一个因素的变化。

9. Alliance：联盟这个词也可以用来描述两个或更多个因素之间的一种关系，它们共同行动以实现共同的目标。

10. Coherence：一致性这个词也可以用来描述两个或更多个因素之间的某种关系，它们在某种程度上是相互关联的，形成一个整体。

常用短语：

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.