correlations

简明释义

英[ˌkɒrəˈleɪʃənz]美[ˌkɔrəˈleɪʃənz]

n. 统计，[数]相关（correlation 复数形式）；相互关系

英英释义

单词用法

同义词

反义词

例句

1.That said, they certainly didn't see any correlations with harm!

那就是说，他们根本没看到任何危害！

2.There are often data correlations among columns.

列之间还有关联。

3.Firms are now studying how genes interact, looking for correlations that might be used to determine the causes of disease or predict a drug's efficacy.

公司正在研究基因是如何相互作用的，以此寻找可用来判断疾病的起因或者预测药物功效的基因相关性。

4.Statistical methods were used to examine the correlations among the various components in the pathways.

以统计学的研究考察上述互动过程中各个要素之间的关系。

5.However, those correlations don't prove cause and effect.

然而，这些相关性并不能证明因果关系。

6.Time is replaced by correlations.

时间被相关性代替了。

7.Recent research finds positive correlations with this honest show of emotion.

最近的研究发现诚恳情感表现的正相关性。

8.Software project managers do not have to calculate the correlations between projects or sub groups of projects.

软件项目经理不必要计算项目或项目子族之间的关联。

9.The study highlighted the correlations between climate change and extreme weather events.

该研究强调了气候变化与极端天气事件之间的相关性。

10.Researchers found significant correlations between exercise and mental health.

研究人员发现锻炼与心理健康之间存在显著的相关性。

11.The analysis showed several correlations among income levels and education.

分析显示收入水平与教育之间存在几个相关性。

12.Statistical software can help identify correlations in large datasets.

统计软件可以帮助识别大型数据集中的相关性。

13.There are strong correlations between smoking and lung cancer rates.

吸烟与肺癌发病率之间存在强烈的相关性。

作文

In the field of statistics and data analysis, understanding the concept of correlations (相关性) is crucial for drawing meaningful conclusions from data. A correlation refers to a statistical measure that expresses the extent to which two variables are linearly related. This relationship can be positive, negative, or nonexistent, and it plays a significant role in various disciplines, including psychology, economics, and health sciences.For instance, consider a study examining the relationship between exercise and mental health. Researchers may find a strong positive correlation (相关性) between the amount of physical activity individuals engage in and their reported levels of happiness. This suggests that as exercise increases, so does the sense of well-being among participants. However, it is essential to note that correlation does not imply causation; while there may be a relationship, it does not mean that one variable directly causes the other.On the other hand, a negative correlation (相关性) can also be observed in different contexts. For example, a research study may reveal that increased screen time is negatively correlated with academic performance among students. In this case, as screen time rises, academic performance tends to decline. Understanding such correlations (相关性) can help educators and parents devise strategies to manage students' screen time effectively.Moreover, correlations (相关性) are not limited to straightforward relationships. In many cases, multiple variables interact in complex ways, leading to what statisticians refer to as multicollinearity. For example, in a study examining factors influencing job satisfaction, researchers may find that salary, workplace environment, and work-life balance all exhibit significant correlations (相关性) with overall job satisfaction. However, disentangling these relationships can be challenging, as they often influence each other.To analyze correlations (相关性), researchers commonly use Pearson's correlation coefficient, which quantifies the strength and direction of a linear relationship between two variables. The value ranges from -1 to +1, where -1 indicates a perfect negative correlation (相关性), +1 indicates a perfect positive correlation (相关性), and 0 indicates no correlation at all. This quantitative approach allows researchers to make informed decisions based on empirical evidence.Furthermore, visualizing correlations (相关性) through scatter plots can provide immediate insights into the relationships between variables. Each point on the plot represents an observation, and the overall pattern can reveal whether a correlation (相关性) exists. For example, if the points tend to cluster around a line with a positive slope, it indicates a positive correlation (相关性). Conversely, if the points cluster around a line with a negative slope, it signifies a negative correlation (相关性).In conclusion, the concept of correlations (相关性) is fundamental in many fields of study. By recognizing and analyzing these relationships, researchers can gain valuable insights that inform decision-making and policy development. However, it is vital to approach correlations (相关性) with caution, ensuring that conclusions drawn are supported by rigorous analysis and do not overstate the implications of the data. As we continue to explore the vast amounts of data available today, understanding correlations (相关性) will remain a key skill for anyone involved in research or data analysis.

在统计学和数据分析领域，理解correlations（相关性）的概念对于从数据中得出有意义的结论至关重要。correlation指的是一种统计测量，表示两个变量之间线性关系的程度。这种关系可以是正向的、负向的或不存在的，并且在心理学、经济学和健康科学等多个学科中发挥着重要作用。例如，考虑一项研究，考察锻炼与心理健康之间的关系。研究人员可能会发现个体参与的体育活动量与其报告的幸福感之间存在强烈的正向correlation（相关性）。这表明，随着锻炼的增加，参与者的幸福感也会增加。然而，必须注意的是，相关性并不意味着因果关系；虽然可能存在关系，但并不意味着一个变量直接导致另一个变量。另一方面，在不同的背景下也可以观察到负向correlation（相关性）。例如，一项研究可能揭示，增加的屏幕时间与学生的学业表现呈负相关。在这种情况下，随着屏幕时间的增加，学业表现往往会下降。理解这样的correlations（相关性）可以帮助教育工作者和家长制定有效管理学生屏幕时间的策略。此外，correlations（相关性）并不仅限于简单的关系。在许多情况下，多种变量以复杂的方式相互作用，导致统计学家所称的多重共线性。例如，在一项研究中，考察影响工作满意度的因素，研究人员可能会发现薪资、工作环境和工作与生活平衡均与整体工作满意度表现出显著的correlations（相关性）。然而，解开这些关系可能具有挑战性，因为它们往往相互影响。为了分析correlations（相关性），研究人员通常使用皮尔逊相关系数，该系数量化两个变量之间线性关系的强度和方向。该值范围从-1到+1，其中-1表示完全负相关，+1表示完全正相关，而0则表示没有相关性。这种定量方法使研究人员能够根据实证证据做出明智的决策。此外，通过散点图可视化correlations（相关性）可以提供对变量之间关系的直接洞察。图中的每个点代表一个观察值，整体模式可以揭示是否存在correlation（相关性）。例如，如果点倾向于聚集在一条正斜率的线上，则表明存在正向correlation（相关性）。反之，如果点聚集在一条负斜率的线上，则表示存在负向correlation（相关性）。总之，correlations（相关性）的概念在许多研究领域中是基础性的。通过识别和分析这些关系，研究人员可以获得有价值的见解，从而为决策和政策制定提供信息。然而，必须谨慎对待correlations（相关性），确保得出的结论得到严格分析的支持，并且不会夸大数据的含义。随着我们继续探索当今可用的大量数据，理解correlations（相关性）将始终是任何参与研究或数据分析的人的关键技能。