probit

简明释义

英[ˈprɒbɪt]美[ˈprɒbɪt]

n. [统物] 概率单位

英英释义

单词用法

同义词

反义词

例句

1.Figure 4 represents the SOA view of the PROBIT architecture.

图4表示PROBIT体系结构的soa视图。

2.The regression analysis showed that there was significantly positive correlation between probit and logarithm of concentrations.

回归分析表明，两种离子的概率单位与浓度对数之间存在着极显著的正相关。

3.Implementing the PROBIT architecture with IBM products.

使用IBM产品实现PROBIT体系结构。

4.The PROBIT architecture in an SOA world.

soa世界中的PROBIT体系结构。

5.Finally, we'll present the NPA use case scenario from the IBM enterprise, and describe a reference implementation using PROBIT to manage the NPA process.

最后，我们将介绍来自IBM企业的NPA用例场景，并描述使用PROBIT来管理npa流程的参考实现。

6.The next section describes in more detail the IBM products used to implement the PROBIT architecture.

下一个部分将更详细地描述用于实现 PROBIT 体系结构的IBM产品。

7.The connectivity layer mainly highlights a broker component within the context of the PROBIT architecture.

在PROBIT体系结构的上下文中，连接层主要突出体现了一个代理组件。

8.To study influence factors of customer complaint, binary choice Probit model and binary choice Logit model of customer complaint are built, and two numerical examples are given, respectively.

对顾客抱怨的影响因素进行研究，分别构建了顾客抱怨的二元选择Logit、Probit模型，并给出了两个实例。

9.In this study, we applied a probit regression to estimate the probability of a customer purchasing a product.

在这项研究中，我们应用了probit回归来估计客户购买产品的概率。

10.The probit function is commonly used in biostatistics for dose-response modeling.

probit函数在生物统计学中常用于剂量反应建模。

11.Using a probit analysis, we found that the drug had a significant effect at higher doses.

通过使用probit分析，我们发现该药物在较高剂量下有显著效果。

12.The researcher used a probit model to analyze the effects of different variables on the likelihood of success.

研究人员使用probit模型分析不同变量对成功可能性的影响。

13.The results from the probit model indicated a strong correlation between income and education level.

probit模型的结果表明收入与教育水平之间存在强相关性。

作文

In the world of statistics and econometrics, the term probit plays a significant role in modeling binary outcome variables. The probit model is particularly useful when researchers need to analyze situations where the dependent variable can take on only two possible outcomes, such as success or failure, yes or no, or 1 and 0. This binary nature of the dependent variable makes the probit model an essential tool for understanding various phenomena in social sciences, health studies, and economics.The probit model is based on the cumulative distribution function of the standard normal distribution. In simpler terms, it transforms the probabilities of the binary outcomes into a format that can be analyzed using linear regression techniques. By doing so, it allows researchers to estimate the likelihood of a particular event occurring based on one or more independent variables. For instance, in a study examining the factors that influence whether individuals choose to buy a product, the probit model can help identify how variables such as income, age, and education level affect the probability of purchase.One of the key advantages of using the probit model is its ability to handle situations where the relationship between the independent variables and the binary outcome is not linear. This flexibility makes it a preferred choice among statisticians and researchers when dealing with real-world data, which often do not conform to simple linear assumptions. Moreover, the probit model provides estimates that are easily interpretable in terms of probabilities, making it accessible for practitioners who may not have extensive statistical training.However, like any statistical model, the probit model has its limitations. One major concern is the assumption of normality in the error terms. If this assumption does not hold true, the estimates produced by the probit model may be biased or inconsistent. Additionally, the probit model may not perform well when there is a high degree of multicollinearity among the independent variables, which can complicate the interpretation of results.Despite these challenges, the probit model remains a powerful tool for researchers. It is widely used in various fields, including finance, medicine, and political science, to analyze decision-making processes. For example, in finance, analysts might employ the probit model to assess the likelihood of loan default based on borrower characteristics. In medicine, researchers could use it to examine the probability of patients adhering to treatment regimens based on demographic and clinical factors.In conclusion, the probit model is an invaluable resource for analyzing binary outcomes in research. Its foundation in the normal distribution allows for a nuanced understanding of how different factors influence decisions and behaviors. While it is essential to remain aware of its limitations, the probit model's capacity to provide meaningful insights into complex issues makes it a staple in the toolkit of statisticians and researchers alike. As we continue to explore the intricacies of human behavior and decision-making, tools like the probit model will undoubtedly play a crucial role in advancing our understanding of the world around us.

在统计学和计量经济学的世界中，术语probit在建模二元结果变量方面发挥着重要作用。probit模型特别适用于研究依赖变量只能取两个可能结果的情况，例如成功或失败、是或否，或1和0。这种二元特性的依赖变量使得probit模型成为理解社会科学、健康研究和经济学中各种现象的重要工具。probit模型基于标准正态分布的累积分布函数。简单来说，它将二元结果的概率转化为可以使用线性回归技术分析的格式。通过这样做，它允许研究人员根据一个或多个自变量估计特定事件发生的可能性。例如，在一项研究中，研究了影响个人选择购买产品的因素，probit模型可以帮助识别收入、年龄和教育水平等变量如何影响购买的概率。使用probit模型的一个主要优点是它能够处理自变量与二元结果之间关系不呈线性的情况。这种灵活性使其成为统计学家和研究人员在处理现实世界数据时的首选，因为现实世界的数据通常不符合简单的线性假设。此外，probit模型提供的估计值易于以概率形式解释，使得那些可能没有广泛统计培训的从业者也能理解。然而，像任何统计模型一样，probit模型也有其局限性。一个主要问题是对误差项正态性的假设。如果这一假设不成立，probit模型产生的估计可能会偏倚或不一致。此外，当自变量之间存在高度多重共线性时，probit模型的表现可能不佳，这可能会使结果的解释变得复杂。尽管面临这些挑战，probit模型仍然是研究人员的强大工具。它被广泛应用于金融、医学和政治科学等各个领域，以分析决策过程。例如，在金融领域，分析师可能会利用probit模型评估借款人特征对贷款违约的可能性。在医学中，研究人员可以使用它来检查患者根据人口统计和临床因素遵循治疗方案的概率。总之，probit模型是分析研究中二元结果的宝贵资源。它基于正态分布的基础使我们能够深入理解不同因素如何影响决策和行为。虽然必须意识到其局限性，但probit模型能够提供对复杂问题的有意义见解，使其成为统计学家和研究人员工具箱中的重要组成部分。在我们继续探索人类行为和决策的复杂性时，像probit模型这样的工具无疑将在推动我们对周围世界的理解方面发挥关键作用。