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相关概念视频

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
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The X̄ Chart00:58

The X̄ Chart

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The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
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Interpreting X̄ Charts01:13

Interpreting X̄ Charts

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Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
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Regression Toward the Mean01:52

Regression Toward the Mean

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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The R Chart01:02

The R Chart

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In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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相关实验视频

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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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记忆类型 贝叶斯适应性最大-EWMA控制图对韦布尔过程的控制图.

Abdullah A Zaagan1, Imad Khan2, Amel Ayari-Akkari3

  • 1Department of Mathematics, Faculty of Science, Jazan University, P.O. Box 2097, 45142, Jazan, Saudi Arabia.

Scientific reports
|April 18, 2024
PubMed
概括

本研究引入了一种新的贝叶斯适应性最大指数加权移动平均线 (Max-EWMA) 控制图,用于监测非正常过程. 拟议的图表有效地检测了工艺转移,超过了半导体制造中现有的方法.

关键词:
运行时间的平均长度.贝叶斯的方法是贝叶斯的方法.控制图表中的控制图表.逆响应函数是一种反响函数.马克斯-EWMAMA 的时间.韦布尔的过程是韦布尔的过程.

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科学领域:

  • 统计过程控制 统计过程控制
  • 质量工程 质量工程
  • 工业统计 工业统计 工业统计

背景情况:

  • 同时监测工艺平均值和分散是至关重要的,特别是对于正常分布.
  • 现有的方法往往假定正常性,限制其应用到非正常的过程.
  • 有效监测非正常过程需要专门的控制图表技术.

研究的目的:

  • 介绍一个新的贝叶斯适应性最大指数加权移动平均线 (Max-EWMA) 控制图.
  • 共同监测非正常过程的平均值和分散,特别是那些遵循韦布尔分布的过程.
  • 评估拟议图表的性能与现有的Max-EWMA图表相比.

主要方法:

  • 利用了对韦布尔分布式过程的逆响应函数.
  • 员工平均运行长度 (ARL) 和运行长度标准偏差 (SDRL) 用于绩效评估.
  • 将拟议的贝叶斯式Max-EWMA图与传统的Max-EWMA图进行比较.

主要成果:

  • 拟议的贝叶斯马克斯-EWMA控制图表在检测失控信号方面表现出卓越的灵敏度.
  • 该图表显示了各种损失函数 (LF) 下的韦布尔过程的有效性能.
  • 一个关于半导体硬过程的案例研究验证了图表的实际适用性和快速检测能力.

结论:

  • 新的贝叶斯适应性Max-EWMA控制图对于监测非正常过程非常有效.
  • 与现有方法相比,拟议的图表在检测过程偏差方面提供了显著的改进.
  • 这有助于加强半导体制造等行业的工艺监测和质量控制.