bivariate

简明释义

英[baɪˈveərɪɪt]美[baɪˈveəriˌeɪt;baɪˈveəriɪt]

adj. 二变量的

英英释义

单词用法

同义词

反义词

例句

1.Two classes of general bivariate interpolating frames are established by introducing multiple parameters.

本文通过引进多参数建立了二元插值的一般框架。

2.Bivariate survival analysis has received substantial attentions due to its wide applications.

双变数存活分析已被广泛应用于生物医学的研究。

3.New topics, such as methods for clinical diagnostic testing, and univariate, bivariate, and multivariate techniques for survival analysis will also be covered.

新的主体，像是临床诊断测试的方法，和存活分析的单变量、双变量和多变量的技术也将会涵盖在其中。

4.Objective: To study a bivariate risk model with variable premium rate.

前言：目的研究一类可变保费的双险种风险模型。

5.And the three optimization problems are solved respectively by the conjugate gradient method, the adaptive bivariate shrinking and the gradient descent method.

并提出分别采用共轭梯度法、二元自适应收缩法以及梯度下降法对以上优化问题求解。

6.Wireless Sensor Network (WSN) was usually vulnerable to conspiracy attack from its adversaries when using bivariate polynomial key pre-distribution protocol.

无线传感器网络在应用二元多项式密钥预分配协议时，通常容易遭受到敌方的合谋攻击。

7.The inner mold support of a knee pipe is composed of the semi-circular arc-shaped support frames and a bivariate arc pad mold.

弯管内模支撑由半圆弧形支撑架与双变弧垫模组成。

8.In statistics, a bivariate analysis is used to determine the relationship between two variables.

在统计学中，bivariate（双变量）分析用于确定两个变量之间的关系。

9.The bivariate regression model helps in predicting outcomes based on two independent variables.

bivariate（双变量）回归模型帮助根据两个自变量预测结果。

10.Researchers often utilize bivariate correlation to explore how two different factors are related.

研究人员经常利用bivariate（双变量）相关性来探讨两个不同因素之间的关系。

11.A bivariate distribution can provide insights into the joint behavior of two random variables.

bivariate（双变量）分布可以提供关于两个随机变量联合行为的洞察。

12.In a bivariate plot, you can visualize the relationship between two continuous variables.

在bivariate（双变量）图中，您可以可视化两个连续变量之间的关系。

作文

In the field of statistics, the term bivariate refers to the analysis of two variables simultaneously. Understanding bivariate relationships is crucial for identifying patterns and making predictions based on data. For instance, when researchers study the relationship between hours studied and exam scores, they are engaging in bivariate analysis. By examining how these two variables interact, we can gain insights into educational outcomes and effectiveness. One common method of exploring bivariate relationships is through scatter plots. A scatter plot visually represents the values of two variables, allowing researchers to observe any potential correlation. If the points on the scatter plot tend to cluster along a line, this suggests a strong bivariate relationship. Conversely, if the points are scattered randomly, it indicates little to no correlation. This visual tool is essential for anyone looking to grasp the concept of bivariate analysis effectively. Moreover, statistical techniques such as Pearson’s correlation coefficient can quantify the strength and direction of bivariate relationships. This coefficient ranges from -1 to 1, where values close to 1 indicate a strong positive correlation, values close to -1 indicate a strong negative correlation, and values around 0 suggest no correlation at all. Understanding these coefficients helps researchers make informed decisions based on data, further emphasizing the importance of bivariate analysis in various fields, including economics, psychology, and health sciences. In addition to correlation, regression analysis is another powerful tool for examining bivariate relationships. Simple linear regression involves fitting a line to the data points in a scatter plot, allowing researchers to predict the value of one variable based on the other. For example, if we know the number of hours a student has studied, we can use regression analysis to predict their likely exam score. This predictive capability is invaluable in many areas, such as marketing, where businesses can forecast sales based on advertising spend. However, it is essential to recognize that correlation does not imply causation. Just because two variables exhibit a strong bivariate relationship does not mean one causes the other. There may be underlying factors influencing both variables, leading to a spurious relationship. Therefore, researchers must approach bivariate analysis with caution and consider other potential variables that could affect their results. In conclusion, the concept of bivariate analysis is fundamental in the realm of statistics. It provides valuable tools for understanding the interactions between two variables, enabling researchers to identify trends, make predictions, and inform decision-making processes. Whether through scatter plots, correlation coefficients, or regression analysis, the ability to analyze bivariate relationships is crucial for anyone working with data. As we continue to navigate an increasingly data-driven world, mastering bivariate analysis will undoubtedly enhance our ability to interpret information and make evidence-based conclusions.

在统计学领域，术语二元指的是同时分析两个变量。理解二元关系对于识别模式和基于数据做出预测至关重要。例如，当研究人员研究学习时间与考试成绩之间的关系时，他们正在进行二元分析。通过检查这两个变量的相互作用，我们可以获得对教育成果和效果的见解。 探讨二元关系的一种常见方法是散点图。散点图直观地表示两个变量的值，使研究人员能够观察到任何潜在的相关性。如果散点图上的点趋向于沿一条线聚集，这表明存在强烈的二元关系。相反，如果点随机分散，则表示几乎没有相关性。这种视觉工具对于任何希望有效掌握二元分析概念的人来说都是必不可少的。 此外，像皮尔逊相关系数这样的统计技术可以量化二元关系的强度和方向。该系数的范围从-1到1，接近1的值表示强正相关，接近-1的值表示强负相关，接近0的值则表明几乎没有相关性。理解这些系数有助于研究人员根据数据做出明智的决策，进一步强调了二元分析在经济学、心理学和健康科学等多个领域的重要性。 除了相关性，回归分析也是检查二元关系的另一种强大工具。简单线性回归涉及将一条线拟合到散点图中的数据点上，从而使研究人员能够根据一个变量预测另一个变量的值。例如，如果我们知道一个学生学习的小时数，我们可以使用回归分析来预测他们可能的考试成绩。这种预测能力在许多领域中都是无价的，例如在营销中，企业可以根据广告支出来预测销售额。 然而，必须认识到，相关性并不意味着因果关系。仅仅因为两个变量表现出强烈的二元关系，并不意味着一个造成了另一个。可能还有其他潜在因素影响着这两个变量，导致虚假的关系。因此，研究人员在进行二元分析时必须谨慎，并考虑可能影响其结果的其他潜在变量。 总之，二元分析的概念在统计学领域是基础性的。它为理解两个变量之间的相互作用提供了有价值的工具，使研究人员能够识别趋势、做出预测并为决策过程提供信息。无论是通过散点图、相关系数还是回归分析，分析二元关系的能力对于任何处理数据的人来说都是至关重要的。随着我们继续在一个日益数据驱动的世界中航行，掌握二元分析无疑将增强我们解读信息和做出基于证据的结论的能力。