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相关概念视频

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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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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Interpreting X̄ Charts01:13

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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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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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Probability Distributions01:32

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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相关实验视频

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Design and Optimization Strategies of a High-Performance Vented Box
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使用半正常和半指数功率分布的控制图,使用重复采样.

Muhammad Naveed1,2, Muhammad Azam3, Nasrullah Khan4

  • 1Department of Statistics, National College of Business Administration and Economics, Lahore, 54660, Pakistan.

Scientific reports
|January 3, 2024
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概括

本研究引入了新的属性控制图 (ACC) 用于使用时间截止寿命测试监测制造缺陷. 基于半指数功率分布 (HEPD) 的图表在检测工艺转移方面表现出卓越的性能.

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科学领域:

  • 工业工程 工业工程 工业工程
  • 统计质量控制 统计质量控制
  • 可靠性工程可靠性工程

背景情况:

  • 制造工艺需要可靠的方法来监控有缺陷的产品.
  • 传统的控制图表可能无法完全捕捉整个生命周期的数据特征.
  • 时间截止寿命测试提供了一个框架,用于分析有限的观察期的数据.

研究的目的:

  • 开发和评估属性控制图 (ACC) 用于监控制造中有缺陷的物品.
  • 根据特定生命周期分布,使用时间截止寿命测试 (TTLT) 量身定制这些图表.
  • 根据重复采样方案 (RSS) 评估拟议图表的性能.

主要方法:

  • 基于半常态分布 (HND) 和半指数功率分布 (HEPD) 的ACC的开发.
  • 应用时间截止寿命测试 (TTLT) 和重复采样方案 (RSS).
  • 使用在控制和控制之外的场景的平均运行长度 (ARL) 计算进行性能评估,考虑参数转移.

主要成果:

  • 基于HEPD的ACC在检测过程转移方面显著优于基于HND和基于指数分布 (ED) 的ACC,以较低的ARL值为证据.
  • 基于HND的ACC也显示了与基于ED的ACC在TTLT和RSS下相比更好的有效性.
  • 模拟测试和现实实施证实了拟议的控制图的实际适用性.

结论:

  • 提出的基于HEPD的属性控制图表对于监测制造质量非常有效.
  • 基于HND的ACC为质量控制应用提供了有价值的替代方案.
  • 这些先进的控制图表增强了在制造环境中的缺陷检测和工艺监控.