multivariate

简明释义

英[ˌmʌltɪˈveərɪɪt]美[ˌmʌltɪˈværɪeɪt]

adj. [数][统计] 多元的；[数] 多变量的

n. 多元；多变量

英英释义

单词用法

同义词

反义词

例句

1.This paper mainly adopts the factor analysis and cluster analysis in the multivariate statistical analysis.

本文主要运用多元统计分析中的因子分析和聚类分析方法。

2.Appling the method of mathematical regression analysis, one multivariate regression equation are given out.

用数学回归的方法推导出了一个多元回归方程。

3.Results from multivariate analyses showed that complicated surgery dramatically increased the risk of endophthalmitis.

多因素分析的结果表明手术并发症明显增加了眼内炎的发病率。

4.Other companies that offer multivariate testing and optimization include Omniture and Accenture.

其他提供多元化测试和优化的公司还包括Omniture和Accenture。

5.Multivariate analysis showed that only the complete resection was a useful index to determine the survival rate.

多因素分析只有完整切除为决定生存率有意义指标。

6.If you require sales copy, you should only work with a web copywriter with knowledge in split and multivariate testing.

如果你需要销售文案，你应该找一个了解分离测试和多元测试的知识的文案。

7.Multivariate data visualization is an important way of understand multivariate data.

多元数据可视化是理解多元数据的一种重要手段。

8.By applying multivariate techniques, scientists can analyze complex data sets with multiple variables.

通过应用多变量技术，科学家可以分析具有多个变量的复杂数据集。

9.In the field of finance, multivariate models are often used to predict stock prices based on various economic indicators.

在金融领域，多变量模型通常用于根据各种经济指标预测股票价格。

10.The researchers conducted a multivariate analysis to understand the factors affecting student performance.

研究人员进行了多变量分析，以了解影响学生表现的因素。

11.A multivariate approach is essential in understanding the interplay between different health factors.

在理解不同健康因素之间的相互作用时，采用多变量方法是必不可少的。

12.The marketing team utilized multivariate testing to determine which advertisement was more effective.

市场营销团队利用多变量测试来确定哪个广告更有效。

作文

In the realm of data analysis, the term multivariate refers to the examination and interpretation of multiple variables simultaneously. This approach is crucial for understanding complex phenomena that cannot be captured by univariate or bivariate analyses alone. For instance, in the field of economics, a researcher might want to study the effects of various factors such as income, education level, and employment status on consumer spending. By employing a multivariate analysis, the researcher can identify how these variables interact with each other and influence spending patterns collectively.One of the most common methods used in multivariate analysis is multiple regression, which allows researchers to predict the value of a dependent variable based on the values of several independent variables. For example, if a company wants to understand the factors affecting its sales, it might consider variables like advertising expenditure, pricing strategy, and product quality. By analyzing these factors together through a multivariate approach, the company can make more informed decisions about where to allocate resources for maximum impact.In addition to multiple regression, there are other multivariate techniques such as factor analysis, cluster analysis, and principal component analysis. Factor analysis helps researchers identify underlying relationships between variables, while cluster analysis groups similar observations together based on their characteristics. Principal component analysis, on the other hand, reduces the dimensionality of the data set, making it easier to visualize and interpret complex relationships among variables.The importance of multivariate analysis extends beyond academic research; it is widely used in various industries including healthcare, marketing, and finance. For instance, in healthcare, multivariate analysis can be employed to assess the effectiveness of different treatments while controlling for patient characteristics such as age, gender, and pre-existing conditions. This ensures that the results are not biased by confounding factors, leading to more accurate conclusions about treatment efficacy.In marketing, companies utilize multivariate testing to optimize their campaigns. By testing different combinations of advertisements, target audiences, and channels, marketers can determine which elements work best together to drive engagement and conversions. This data-driven approach helps businesses maximize their return on investment and tailor their strategies to meet the needs of their customers.Despite its advantages, conducting multivariate analysis can be challenging. It requires a solid understanding of statistical concepts and the ability to interpret complex data sets. Moreover, the presence of multicollinearity—where independent variables are highly correlated—can complicate the analysis and lead to misleading results. Therefore, it is essential for researchers and analysts to approach multivariate analysis with caution and rigor, ensuring that they validate their models and findings appropriately.In conclusion, the concept of multivariate analysis plays a pivotal role in various fields by enabling a comprehensive understanding of the interplay between multiple variables. Whether in economics, healthcare, marketing, or any other domain, the ability to analyze and interpret complex data sets is invaluable. As we continue to generate vast amounts of data, mastering multivariate techniques will be increasingly important for making informed decisions and driving progress in our understanding of multifaceted issues.

在数据分析领域，术语多变量指的是同时对多个变量进行检查和解释。这种方法对于理解复杂现象至关重要，因为这些现象无法仅通过单变量或双变量分析来捕捉。例如，在经济学领域，研究人员可能想要研究收入、教育水平和就业状况等各种因素对消费者支出的影响。通过采用多变量分析，研究人员可以识别这些变量如何相互作用并共同影响支出模式。在多变量分析中，最常用的方法之一是多重回归，它允许研究人员根据多个自变量的值预测因变量的值。例如，如果一家公司想要了解影响其销售的因素，它可能会考虑广告支出、定价策略和产品质量等变量。通过通过多变量方法分析这些因素，公司可以更明智地决定在哪里分配资源以获得最大影响。除了多重回归，还有其他多变量技术，如因子分析、聚类分析和主成分分析。因子分析帮助研究人员识别变量之间的潜在关系，而聚类分析则基于特征将相似的观察结果分组。另一方面，主成分分析减少数据集的维度，使得更容易可视化和解释变量之间的复杂关系。多变量分析的重要性超越了学术研究；它在医疗保健、市场营销和金融等各个行业广泛使用。例如，在医疗保健中，可以使用多变量分析来评估不同治疗方法的有效性，同时控制患者的年龄、性别和既往疾病等特征。这确保了结果不受混杂因素的偏倚，从而得出关于治疗效果的更准确结论。在市场营销中，公司利用多变量测试来优化其活动。通过测试不同组合的广告、目标受众和渠道，营销人员可以确定哪些元素能够最佳地协同工作以推动参与和转化。这种数据驱动的方法帮助企业最大化投资回报，并根据客户的需求量身定制其策略。尽管有其优势，进行多变量分析可能具有挑战性。它需要对统计概念有扎实的理解，并能够解释复杂的数据集。此外，多重共线性（自变量高度相关）可能会使分析复杂化并导致误导性结果。因此，研究人员和分析师在进行多变量分析时必须谨慎和严谨，确保他们适当地验证其模型和发现。总之，多变量分析的概念在各个领域发挥着关键作用，使人们能够全面理解多个变量之间的相互作用。无论是在经济学、医疗保健、市场营销还是其他任何领域，分析和解释复杂数据集的能力都是无价的。随着我们继续生成大量数据，掌握多变量技术对于做出明智决策和推动我们对多方面问题的理解将变得越来越重要。