covariation

简明释义

英[ˌkəʊveərɪˈeɪʃən]美[koʊveərɪˈeɪʃən]

n. [数] 共变；[遗] 相关变异

英英释义

单词用法

同义词

反义词

例句

1.This paper is based on the project named the Multimedia Network Performance Prediction Model based on Covariation-orthogonality and United Optimization which is supported by NSFC.

本论文是基于国家自然科学基金项目“基于共变正交和联合优化的多媒体网络性能预测模型”的研究需要而展开的。

2.It reveals that the interspecific covariation can not only display the probability of species coexistence but also indicate the direction and extent to which the quantity of species changed.

研究结果表明，种间协变不仅能够反映出物种间共存的机率，而且能够反映出其相对数量变化的趋势和程度。

3.The covariation of language and society is a fundamental concept in the covariation theory of sociolinguistics.

语言和社会的共变是社会语言学共变论的一个基本观点。

4.The covariation between the potential cause and the result and the causal power of the potential cause are two most important factors in causal judgement.

研究使用不同方式呈现了其它可能原因，研究了这种不同的呈现方式对被试使用共变信息和经验信息进行因果判断的影响。

5.It reveals that the interspecific covariation can not only display the probability of species coexistence but also indicate the direction and extent to which the quantity of species changed.

研究结果表明，种间协变不仅能够反映出物种间共存的机率，而且能够反映出其相对数量变化的趋势和程度。

6.The algorithm constructs matrixes by using covariation or covariation coefficient and estimates bearing and range of near-field sources by rooting method.

本方法利用协变异(或协变异系数)构造矩阵，并通过求根的方法估计近场源的方向角和距离参数。

7.In statistics, covariation 共变 helps to understand relationships between variables.

在统计学中，共变有助于理解变量之间的关系。

8.Researchers found a significant covariation 共变 between exercise frequency and overall health.

研究人员发现锻炼频率与整体健康之间存在显著的共变。

9.The study examined the covariation 共变 between temperature and ice cream sales.

这项研究考察了温度与冰淇淋销售之间的共变。

10.The covariation 共变 of social media usage and mental health issues is a growing concern.

社交媒体使用与心理健康问题的共变是一个日益关注的问题。

11.The covariation 共变 of income levels and education attainment was analyzed in the report.

报告中分析了收入水平与教育程度之间的共变。

作文

In the realm of statistics and data analysis, the concept of covariation plays a crucial role in understanding the relationship between two or more variables. Simply put, covariation refers to the way two variables change together. When one variable increases or decreases, how does the other variable respond? This relationship can be positive, negative, or nonexistent, and it is essential for researchers and analysts to grasp these dynamics when interpreting data.For instance, consider a study examining the relationship between hours studied and exam scores among students. If we observe that as the number of hours studied increases, the exam scores also tend to rise, we can say there is a positive covariation between these two variables. Conversely, if we find that as the temperature rises, the sales of hot chocolate decrease, this indicates a negative covariation. Understanding these patterns helps educators formulate better teaching strategies and aids students in managing their study habits effectively.Moreover, covariation is not limited to just two variables; it can extend to multiple variables in complex datasets. In fields such as economics, psychology, and environmental science, researchers often analyze how various factors interact with one another. For example, an economist might investigate how changes in interest rates, inflation, and unemployment rates exhibit covariation. By studying these interrelationships, economists can make informed predictions about future market trends and policy impacts.The mathematical representation of covariation is typically expressed through covariance, which quantifies the degree to which two variables change together. A positive covariance indicates that the variables tend to increase or decrease in tandem, while a negative covariance suggests an inverse relationship. However, it is important to note that covariance alone does not imply causation. Just because two variables exhibit covariation does not mean that one causes the other to change. To establish causation, further investigation is required, often involving controlled experiments or longitudinal studies.In practical applications, covariation is vital in fields like machine learning and predictive analytics. Algorithms often rely on understanding the relationships between variables to make accurate predictions. For instance, a recommendation system might analyze user behavior data to determine how certain features or products exhibit covariation, leading to personalized suggestions for users.In conclusion, the concept of covariation is fundamental in statistical analysis, allowing researchers and analysts to explore the relationships between variables. Whether in educational research, economic forecasting, or machine learning, understanding how variables covary provides valuable insights that can drive decision-making and strategy development. As we continue to collect and analyze vast amounts of data, mastering the concept of covariation will remain essential for drawing meaningful conclusions and making informed predictions in our increasingly data-driven world.

在统计学和数据分析领域，协变的概念在理解两个或多个变量之间的关系中发挥着至关重要的作用。简单来说，协变是指两个变量如何共同变化。当一个变量增加或减少时，另一个变量如何响应？这种关系可以是正向的、负向的或不存在的，研究人员和分析师在解释数据时掌握这些动态是至关重要的。例如，考虑一项研究，考察学生学习时间与考试成绩之间的关系。如果我们观察到，随着学习时间的增加，考试成绩也往往提高，我们可以说这两个变量之间存在正向协变。相反，如果我们发现，当温度升高时，热巧克力的销售量降低，这表明存在负向协变。理解这些模式有助于教育工作者制定更好的教学策略，并帮助学生有效管理他们的学习习惯。此外，协变不仅限于两个变量；它可以扩展到复杂数据集中的多个变量。在经济学、心理学和环境科学等领域，研究人员经常分析各种因素如何相互作用。例如，经济学家可能会研究利率、通货膨胀和失业率的变化如何表现出协变。通过研究这些相互关系，经济学家可以对未来市场趋势和政策影响做出明智的预测。协变的数学表示通常通过协方差来表达，它量化了两个变量共同变化的程度。正协方差表明变量倾向于同步增加或减少，而负协方差则表明一种逆向关系。然而，需要注意的是，仅靠协方差并不能暗示因果关系。仅仅因为两个变量表现出协变，并不意味着一个变量导致另一个变量发生变化。要建立因果关系，需要进一步调查，通常涉及控制实验或纵向研究。在实际应用中，协变在机器学习和预测分析等领域至关重要。算法通常依赖于理解变量之间的关系以做出准确的预测。例如，推荐系统可能会分析用户行为数据，以确定某些特征或产品如何表现出协变，从而为用户提供个性化建议。总之，协变的概念在统计分析中是基础，使研究人员和分析师能够探讨变量之间的关系。无论是在教育研究、经济预测还是机器学习中，理解变量如何协变提供了宝贵的洞察力，可以推动决策制定和战略发展。随着我们继续收集和分析大量数据，掌握协变的概念将仍然是从我们的数据驱动世界中得出有意义结论和做出明智预测的关键。