correlatable

简明释义

英[ˌkɒrəˈleɪtəbl]美[ˌkɔːrəˈleɪtəbl]

adj. 相关的

英英释义

单词用法

同义词

反义词

例句

1.Through sequence stratigraphy, widely-separated sediments that occur between correlatable unconformities could be compared with each other.

通过层序地层学研究，产生于可对比不整合广泛分离的沉积物可以彼此比较。

2.Through sequence stratigraphy, widely-separated sediments that occur between correlatable unconformities could be compared with each other.

通过层序地层学研究，产生于可对比不整合广泛分离的沉积物可以彼此比较。

3.Widely-separated sediments that occur between correlatable unconformities could be compared with each other.

可以将在相关不整合面之间出现的大范围沉积物相互比较。

4.The data collected from the two studies is highly correlatable, indicating a strong relationship between the variables.

从这两项研究收集的数据是高度相关的，表明变量之间存在强关系。

5.The research aims to find correlatable patterns in consumer behavior across different demographics.

该研究旨在发现不同人口统计中消费者行为的相关模式。

6.In finance, market trends and economic indicators are often correlatable, helping investors make informed decisions.

在金融领域，市场趋势和经济指标通常是相关的，帮助投资者做出明智的决策。

7.In psychology, behaviors and environmental factors are often correlatable in understanding mental health issues.

在心理学中，行为和环境因素通常是相关的，以理解心理健康问题。

8.The findings are correlatable with previous studies, suggesting consistency in the results.

这些发现与之前的研究是相关的，表明结果的一致性。

作文

In the field of research, understanding relationships between different variables is crucial. One term that often comes up in this context is correlatable, which refers to the ability of two or more variables to show a relationship or correlation with each other. This concept is vital in various disciplines, including psychology, economics, and environmental science. For instance, in psychology, researchers often investigate whether there is a correlatable connection between stress levels and academic performance. By examining data from students under varying levels of stress, they can determine if higher stress levels correlate with lower grades. This kind of analysis helps in understanding how external factors influence individual performance.Similarly, in economics, the concept of correlatable variables plays a significant role in market analysis. Economists often study the relationship between unemployment rates and consumer spending. If they find that these two variables are correlatable, it could indicate that higher unemployment leads to decreased consumer spending, which in turn affects the overall economy. This knowledge can guide policymakers in making informed decisions to stimulate economic growth.Environmental science also heavily relies on the idea of correlatable factors. Researchers may look at the relationship between carbon emissions and global temperatures. If they establish a strong correlatable link, it provides evidence supporting the need for policies aimed at reducing emissions to combat climate change. Understanding these correlations is essential for developing effective strategies to protect our environment.However, it is important to remember that correlation does not imply causation. Just because two variables are correlatable does not mean that one causes the other. For example, while there may be a correlatable relationship between ice cream sales and drowning incidents, it would be misleading to conclude that buying ice cream causes drownings. Instead, both may be influenced by a third variable, such as warm weather. Therefore, researchers must exercise caution when interpreting data and ensure they conduct thorough analyses to avoid drawing incorrect conclusions.In conclusion, the term correlatable is fundamental in research across various fields, helping to establish connections between different variables. Whether in psychology, economics, or environmental science, identifying correlatable factors can lead to valuable insights and inform decision-making processes. However, it is crucial to approach these findings critically and understand the limitations of correlation studies. By doing so, we can harness the power of correlatable data to drive progress and improve our understanding of complex issues in our world today.

在研究领域，理解不同变量之间的关系至关重要。一个经常出现的术语是correlatable，它指的是两个或多个变量之间能够显示出关系或相关性的能力。这个概念在心理学、经济学和环境科学等多个学科中都非常重要。例如，在心理学中，研究人员常常调查压力水平与学业表现之间是否存在correlatable的联系。通过检查在不同压力水平下的学生数据，他们可以确定较高的压力水平是否与较低的成绩相关。这种分析有助于理解外部因素如何影响个人表现。同样，在经济学中，correlatable变量的概念在市场分析中发挥着重要作用。经济学家通常研究失业率与消费支出之间的关系。如果他们发现这两个变量是correlatable的，这可能表明较高的失业率导致消费支出减少，从而影响整体经济。这种知识可以指导政策制定者做出明智的决策，以刺激经济增长。环境科学也在很大程度上依赖于correlatable因素的概念。研究人员可能会研究碳排放与全球气温之间的关系。如果他们建立了强烈的correlatable联系，这为支持减少排放以应对气候变化的政策提供了证据。理解这些相关性对于制定有效的保护环境的策略至关重要。然而，重要的是要记住，相关性并不意味着因果关系。仅仅因为两个变量是correlatable的，并不意味着一个导致了另一个。例如，虽然冰淇淋销售和溺水事件之间可能存在correlatable关系，但得出购买冰淇淋会导致溺水的结论是误导性的。相反，这两者可能受到第三个变量的影响，例如天气炎热。因此，研究人员在解释数据时必须谨慎，并确保进行彻底的分析，以避免得出错误的结论。总之，correlatable这一术语在各个领域的研究中都是基础，帮助建立不同变量之间的联系。无论是在心理学、经济学还是环境科学，识别correlatable因素都能带来宝贵的见解，并为决策过程提供信息。然而，关键是要批判性地看待这些发现，并理解相关研究的局限性。通过这样做，我们可以利用correlatable数据的力量，推动进步，提高我们对当今世界复杂问题的理解。