correlator

简明释义

英[ˈkɒrɪleɪtə(r)]美["ˈkɔːrəleɪtər"]

n. [电子] 相关器；[物] 相关因子

英英释义

单词用法

同义词

反义词

例句

1.The theory shows that correlator space is an important factor of measure error.

根据理论分析，相关间隔是影响伪码测距的重要因素。

2.It is up to the third-party application developer to ensure stability of any modification to the original ARM correlator generated by the engine.

这主要是为了让第三方应用程序开发人员确保对引擎产生的原始arm correlator的任何修改的稳定性。

3.A correlator is generated for every root (or edge) transaction and each of its child transactions.

每个根事务和它的每个子事务都可以产生一个correlator。

4.Suppose the mathematical expectation of image signal is zero, the basic properties of the correlator output are given.

假定图象信号的数学期望为零，证明了相关器输出的基本性质。

5.A new binary joint transform correlator (BJTC) with high pass filtering is. proposed.

提出一种新的采用高通滤波的二元联合变换相关器。

6.Explicit correlation can lead to tight coupling if the correlator is infrastructural data that should not be intermixed with business data.

如果相关器是基础设施数据，不应该与业务数据混杂在一起，则显式相关性可能会导致紧密耦合。

7.Using a correlator, the scientists were able to link increased pollution levels to respiratory issues.

通过使用相关器，科学家们能够将污染水平的增加与呼吸问题联系起来。

8.The data analyst used a correlator to identify the relationship between sales and advertising spend.

数据分析师使用了一个相关器来识别销售与广告支出之间的关系。

9.The economic model utilized a correlator to predict future market trends based on historical data.

该经济模型利用了一个相关器，根据历史数据预测未来市场趋势。

10.The software includes a correlator that can analyze large datasets for hidden patterns.

该软件包括一个相关器，可以分析大型数据集中的隐藏模式。

11.In the research study, the correlator helped determine how temperature affects plant growth.

在这项研究中，相关器帮助确定温度如何影响植物生长。

作文

In the realm of data analysis and scientific research, the term correlator (关联器) plays a pivotal role in understanding relationships between different variables. A correlator is essentially a tool or method used to identify and quantify the degree to which two or more variables are related. This relationship can be either positive, negative, or nonexistent, and understanding these correlations can lead to significant insights in various fields such as psychology, economics, and even environmental science.To illustrate the importance of a correlator (关联器), let's consider a simple example from the field of psychology. Researchers often seek to understand the correlation between stress levels and academic performance among students. By employing a correlator, they can analyze data collected from surveys that measure stress levels and academic grades. If the analysis reveals a strong negative correlation, it suggests that as stress levels increase, academic performance tends to decrease. This insight could prompt educational institutions to implement stress management programs to help students improve their performance.Moreover, in the field of economics, a correlator (关联器) is crucial for understanding market trends and consumer behavior. Economists might use a correlator to analyze the relationship between unemployment rates and consumer spending. A positive correlation could indicate that as unemployment decreases, consumer spending increases, which is vital information for policymakers aiming to stimulate economic growth.In addition to its application in social sciences, a correlator (关联器) is also instrumental in environmental studies. For instance, scientists may want to explore the correlation between carbon dioxide levels and global temperatures. By utilizing a correlator, they can analyze historical data and potentially uncover patterns that inform climate change models. Understanding these correlations can guide policy decisions and conservation efforts aimed at mitigating the effects of climate change.However, it is essential to note that correlation does not imply causation. Just because two variables are correlated does not mean that one causes the other. This is a critical distinction that researchers must keep in mind when interpreting the results obtained from a correlator (关联器). For example, if a study finds a correlation between ice cream sales and drowning incidents, it would be erroneous to conclude that buying ice cream causes drowning. Instead, both may be influenced by a third variable, such as warm weather.In conclusion, the concept of a correlator (关联器) is integral to various fields of study, enabling researchers and analysts to uncover meaningful relationships between variables. By leveraging this tool, we can gain valuable insights that drive decision-making and enhance our understanding of complex systems. As we continue to collect and analyze data in an increasingly data-driven world, mastering the use of a correlator will be essential for anyone involved in research and analysis. Whether in psychology, economics, environmental science, or any other field, the ability to identify and interpret correlations can lead to informed conclusions and impactful actions.

在数据分析和科学研究的领域中，术语correlator（关联器）在理解不同变量之间的关系中扮演着关键角色。correlator本质上是用来识别和量化两个或多个变量之间关系的工具或方法。这种关系可以是正相关、负相关或不存在，理解这些相关性可以在心理学、经济学甚至环境科学等各个领域带来重要的见解。为了说明correlator（关联器）的重要性，让我们考虑一个来自心理学领域的简单例子。研究人员常常试图理解学生的压力水平与学业表现之间的相关性。通过使用correlator，他们可以分析从测量压力水平和学业成绩的调查中收集的数据。如果分析结果揭示出强烈的负相关，这表明随着压力水平的增加，学业表现往往会下降。这一见解可能促使教育机构实施压力管理程序，以帮助学生提高表现。此外，在经济学领域，correlator（关联器）对于理解市场趋势和消费者行为至关重要。经济学家可能会使用correlator来分析失业率与消费者支出之间的关系。正相关可能表明随着失业率的降低，消费者支出增加，这对旨在刺激经济增长的政策制定者来说是至关重要的信息。除了在社会科学中的应用，correlator（关联器）在环境研究中也发挥着重要作用。例如，科学家可能希望探讨二氧化碳水平与全球气温之间的相关性。通过利用correlator，他们可以分析历史数据，并可能发现有助于气候变化模型的模式。理解这些相关性可以指导政策决策和保护工作，以减轻气候变化的影响。然而，必须注意的是，相关性并不意味着因果关系。仅仅因为两个变量是相关的，并不意味着一个导致了另一个。这是研究人员在解释从correlator（关联器）获得的结果时必须牢记的关键区别。例如，如果一项研究发现冰淇淋销售与溺水事件之间存在相关性，那么得出购买冰淇淋导致溺水的结论是错误的。相反，两者可能受到第三个变量的影响，例如温暖的天气。总之，correlator（关联器）的概念在各个研究领域中都是不可或缺的，使研究人员和分析师能够揭示变量之间有意义的关系。通过利用这一工具，我们可以获得推动决策和增强我们对复杂系统理解的宝贵见解。随着我们在日益数据驱动的世界中继续收集和分析数据，掌握使用correlator将对任何参与研究和分析的人来说都是必不可少的。无论是在心理学、经济学、环境科学还是其他任何领域，识别和解释相关性的能力都能导致明智的结论和有影响力的行动。