univariate

简明释义

英[ˌjuːnɪˈveərɪɪt]美[junɪ'vɛrɪet]

adj. [数] 单变量的

英英释义

单词用法

同义词

反义词

例句

1.Some of these estimates involve only univariate numerical integrals.

某些估计只涉及到一重数值积分。

2.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。

3.The multivariate analysis controlled for all factors with significant associations emerging from the univariate analysis.

多元分析控制所有在单因素分析中出现的有意义的因素。

4.RESULTS Hypothesis tests for the presuppositions of the univariate ANOVA with repeated measures were given, and a proper software named rep was implemented.

结果给出了重复观测数据一元方差分析的假定条件检验方法，并用软件REP得以实现。

5.The course is an introduction to univariate and multivariate time series models.

本课程是对于单变量与多变量时间序列模型的一个介绍。

6.Univariate and multivariate Cox proportional hazards models were used to identify predictors of survival for patients with pulmonary metastatic melanoma.

该研究的目的是鉴别肺转移性黑色素瘤患者生存的预测指标。

7.For the univariate case, we discuss two kinds of dynamic models: GARCH type model and regime switching model.

对于单变量的情况，本文讨论了两种动态模型：GARCH类型模型和状态转换模型。

8.Univariate linear correlation analysis was used for comparison among the variables.

变量间采用单因素直线相关分析。

9.A univariate distribution shows the probability of different outcomes for one variable.

一个单变量分布显示了一个变量不同结果的概率。

10.The univariate regression model can help us understand how a single predictor affects the outcome.

这个单变量回归模型可以帮助我们理解单个预测变量如何影响结果。

11.The results of the univariate study were significant, indicating a strong relationship.

该单变量研究的结果是显著的，表明存在强关系。

12.In a univariate analysis, we focus on one variable at a time.

在单变量分析中，我们一次关注一个变量。

13.We conducted a univariate analysis of the sales data to identify trends.

我们对销售数据进行了单变量分析，以识别趋势。

作文

In the field of statistics, analysis can be categorized into different types based on the number of variables involved. One such category is known as univariate analysis. The term univariate refers to the examination of a single variable at a time. This type of analysis is crucial for understanding the basic characteristics of data before moving on to more complex analyses involving multiple variables. By focusing solely on one variable, researchers can gain insights into its distribution, central tendency, and variability. For instance, consider a scenario where a researcher is interested in studying the heights of students in a classroom. A univariate analysis would involve collecting data on the heights of each student and then summarizing this information using descriptive statistics such as the mean, median, mode, and standard deviation. These statistics provide a clear picture of the height distribution within that specific group. Moreover, univariate analysis often involves visual representations like histograms or box plots, which help in identifying patterns, outliers, and the overall shape of the data distribution. For example, a histogram displaying the heights of students may reveal whether the data is normally distributed, skewed, or has any significant outliers. This initial exploration is essential because it sets the foundation for further analysis, such as bivariate or multivariate analyses, where relationships between two or more variables are investigated. The importance of univariate analysis cannot be overstated; it serves as a preliminary step that aids in hypothesis generation and informs subsequent research design. By understanding the behavior of individual variables, researchers can formulate more informed hypotheses regarding how these variables might interact with others. For instance, if the height data shows a significant variation among students, a researcher might hypothesize that factors such as nutrition, genetics, or physical activity levels could influence height. In addition to its application in educational settings, univariate analysis is widely used across various fields, including economics, psychology, and healthcare. In economics, a univariate analysis of consumer spending can provide insights into economic trends and consumer behavior. Similarly, in psychology, researchers might conduct univariate analyses to examine the effects of a particular treatment on a single psychological measure, such as anxiety levels. Furthermore, while univariate analysis focuses on one variable, it is essential to recognize its limitations. Since it does not account for relationships between variables, it may overlook important contextual factors that could influence the outcome. Therefore, while univariate analysis is a valuable tool, it should be complemented with other methods to gain a comprehensive understanding of the data. In conclusion, univariate analysis plays a vital role in statistical research by providing a foundational understanding of individual variables. It allows researchers to describe and summarize data effectively, paving the way for deeper investigations into the relationships and interactions among multiple variables. As such, mastering univariate analysis is an essential skill for anyone involved in data analysis and research, enabling them to make informed decisions based on a solid understanding of their data.

在统计学领域，分析可以根据涉及的变量数量进行分类。其中一种分类被称为单变量分析。术语单变量指的是一次只检查一个变量。这种类型的分析对于理解数据的基本特征至关重要，因为它为进行更复杂的涉及多个变量的分析奠定了基础。通过仅关注一个变量，研究人员可以深入了解其分布、集中趋势和变异性。例如，考虑一个研究者有兴趣研究教室中学生身高的场景。单变量分析将涉及收集每位学生的身高数据，然后使用描述性统计量（如均值、中位数、众数和标准差）来总结这些信息。这些统计数据提供了该特定群体身高分布的清晰图景。此外，单变量分析通常涉及直方图或箱线图等可视化表示，这有助于识别模式、异常值以及数据分布的整体形状。例如，显示学生身高的直方图可能揭示数据是否呈正态分布、偏斜或有任何显著的异常值。这种初步探索至关重要，因为它为进一步分析（如双变量或多变量分析）奠定了基础，在这些分析中将调查两个或多个变量之间的关系。单变量分析的重要性不可小觑；它作为一个初步步骤，有助于假设生成，并为后续研究设计提供信息。通过理解单个变量的行为，研究人员可以更有根据地形成关于这些变量如何与其他变量相互作用的假设。例如，如果身高数据在学生中显示出显著的变化，研究人员可能会假设营养、遗传或身体活动水平等因素可能影响身高。除了在教育环境中的应用外，单变量分析还广泛应用于经济学、心理学和医疗保健等各个领域。在经济学中，对消费者支出的单变量分析可以提供有关经济趋势和消费者行为的见解。同样，在心理学中，研究人员可能会进行单变量分析，以检查某种治疗对单一心理测量（如焦虑水平）的影响。此外，虽然单变量分析专注于一个变量，但认识到它的局限性也很重要。由于它不考虑变量之间的关系，因此可能会忽视可能影响结果的重要背景因素。因此，尽管单变量分析是一个有价值的工具，但应与其他方法结合使用，以获得对数据的全面理解。总之，单变量分析在统计研究中发挥着至关重要的作用，通过提供对个别变量的基础理解，使研究人员能够有效地描述和总结数据，为深入调查多个变量之间的关系和互动铺平道路。因此，掌握单变量分析是任何参与数据分析和研究的人的基本技能，使他们能够基于对数据的深刻理解做出明智的决策。