stub-survivor curve

简明释义

残段生存曲线

英英释义

例句

1.The analysis of the data revealed a distinct pattern in the stub-survivor curve 存活曲线, indicating how many products remained functional over time.

数据分析显示，stub-survivor curve 存活曲线中存在明显的模式，指示有多少产品在一段时间内保持功能。

2.The stub-survivor curve 存活曲线 helps engineers understand the failure rates of different components in machinery.

stub-survivor curve 存活曲线帮助工程师了解机器中不同组件的故障率。

3.In our recent study, we used the stub-survivor curve 存活曲线 to predict the lifespan of our new software.

在我们最近的研究中，我们使用了stub-survivor curve 存活曲线来预测我们新软件的使用寿命。

4.The team presented their findings on the stub-survivor curve 存活曲线 at the annual conference, showcasing its importance in quality control.

团队在年会上展示了关于stub-survivor curve 存活曲线的发现，展示了其在质量控制中的重要性。

5.By plotting the stub-survivor curve 存活曲线, we can visually assess the reliability of our products over time.

通过绘制stub-survivor curve 存活曲线，我们可以直观地评估我们产品随时间的可靠性。

作文

In the field of statistics and data analysis, understanding various curves and models is essential for interpreting data accurately. One such important concept is the stub-survivor curve, which plays a significant role in survival analysis and reliability engineering. The stub-survivor curve is primarily used to represent the probability of survival of a system or component over time, particularly when dealing with incomplete data or truncated datasets. This curve helps analysts to visualize how long a particular item is expected to last before it fails or is removed from consideration. The stub-survivor curve is particularly useful in scenarios where traditional survival curves may not provide an accurate representation due to missing information. For instance, in medical research, patients may drop out of a study, leading to incomplete data regarding their survival times. By applying the stub-survivor curve, researchers can estimate the survival probabilities of the remaining participants while accounting for those who were lost to follow-up. This adjustment is crucial for ensuring that the results are not biased and reflect the true survival rates of the population being studied.Moreover, the stub-survivor curve can be applied in various industries, including manufacturing, where it is vital to understand the lifespan of products. In this context, engineers can utilize the curve to predict when machines or components are likely to fail, allowing for better maintenance scheduling and resource allocation. By analyzing the stub-survivor curve, companies can reduce downtime and improve operational efficiency, ultimately leading to cost savings and increased productivity.To construct a stub-survivor curve, one typically begins with a dataset that includes the time until failure for each component or subject under observation. The next step involves estimating the survival function, which represents the probability that a given component will survive beyond a certain time. Once the survival function is established, the stub-survivor curve can be plotted, providing a visual representation of survival probabilities over time.An interesting aspect of the stub-survivor curve is its ability to adapt to different types of data. For example, in cases where the data is censored—meaning that the exact failure time is not known for all subjects—the stub-survivor curve can still provide valuable insights by incorporating the available information. This flexibility makes it a powerful tool in both research and practical applications.In conclusion, the stub-survivor curve is an invaluable concept in statistics that aids in the understanding of survival probabilities in the presence of incomplete data. Its applications span across various fields, from healthcare to engineering, highlighting its versatility and importance. By leveraging the stub-survivor curve, analysts and researchers can make informed decisions based on more accurate survival estimates, ultimately leading to better outcomes in their respective domains.

在统计学和数据分析领域，理解各种曲线和模型对于准确解释数据至关重要。其中一个重要的概念是stub-survivor curve，它在生存分析和可靠性工程中发挥着重要作用。stub-survivor curve主要用于表示系统或组件在时间上的生存概率，特别是在处理不完整数据或截断数据集时。该曲线帮助分析师可视化特定项目在故障或被排除在考虑之外之前预计会持续多久。stub-survivor curve在传统生存曲线可能因缺失信息而无法提供准确表示的场景中特别有用。例如，在医学研究中，患者可能会退出研究，导致有关其生存时间的不完整数据。通过应用stub-survivor curve，研究人员可以估计剩余参与者的生存概率，同时考虑那些失访的患者。这种调整对于确保结果不偏倚并反映所研究人群的真实生存率至关重要。此外，stub-survivor curve还可以应用于各个行业，包括制造业，在这些行业中，了解产品的使用寿命至关重要。在这种情况下，工程师可以利用该曲线预测机器或组件何时可能发生故障，从而更好地安排维护和资源分配。通过分析stub-survivor curve，公司可以减少停机时间，提高运营效率，最终节省成本并增加生产力。构建stub-survivor curve的第一步通常是从包含每个观察对象或组件的故障时间的数据集中开始。下一步涉及估计生存函数，该函数表示给定组件在某一特定时间之后存活的概率。一旦生存函数建立，就可以绘制stub-survivor curve，提供生存概率随时间变化的可视化表示。stub-survivor curve的一个有趣方面是它能够适应不同类型的数据。例如，在数据被删失的情况下——即并非所有对象的确切故障时间都是已知的——stub-survivor curve仍然可以通过结合可用的信息提供有价值的见解。这种灵活性使其成为研究和实际应用中的强大工具。总之，stub-survivor curve是统计学中一个无价的概念，有助于在不完整数据存在的情况下理解生存概率。它的应用跨越多个领域，从医疗保健到工程，突显了其多功能性和重要性。通过利用stub-survivor curve，分析师和研究人员可以根据更准确的生存估计做出明智的决策，最终在各自领域实现更好的结果。