Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

The R Chart01:02

The R Chart

82
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...
82
Interpreting R Charts01:22

Interpreting R Charts

67
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...
67
Quality Control01:05

Quality Control

174
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
174
Random Sampling Method01:09

Random Sampling Method

11.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.2K
The X̄ Chart00:58

The X̄ Chart

124
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...
124
Contaminants and Errors01:16

Contaminants and Errors

94
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
94

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Therapeutic Effects of Glucagon-like Peptide-1 Receptor Agonists in Non-Alcoholic Fatty Liver Disease: A Systematic Review.

International journal of molecular sciences·2026
Same author

Letter to Editor "Comments on self-medication with conventional and herbal medicines in pregnancy: prevalence and factors in Northwest Ethiopia".

Annals of medicine and surgery (2012)·2026
Same author

Virgin coconut oil: A comprehensive review of its health impacts and functional food applications.

Food chemistry: X·2026
Same author

First-principles investigation of Pd-based Kesterites for optoelectronic and photovoltaic applications.

Journal of molecular modeling·2026
Same author

Effects of high-pressure homogenization and enzymatic hydrolysis on the physicochemical properties of gelatin-stabilized tuna oil-based emulsion.

Food chemistry: X·2026
Same author

The first week matters: App-based PROM trajectories and follow-up retention after endoscopic lumbar surgery.

Brain & spine·2026
Same journal

Application of ephrin-B2 loaded glycol chitosan-silk fibroin hydrogel in the treatment of diabetic refractory wounds.

Scientific reports·2026
Same journal

International expert Delphi consensus on thromboprophylaxis in metabolic and bariatric surgery.

Scientific reports·2026
Same journal

Assessing the cross-region knowledge transfer capability of selected deep learning building vectorization methods in the context of available training datasets.

Scientific reports·2026
Same journal

Feasibility and preliminary effects of outdoor versus indoor cognitive-motor therapy in women with Alzheimer's disease: A randomized single-blind pilot study.

Scientific reports·2026
Same journal

Hallmarks of social action in the vocal turn-taking of wild common marmosets (Callithrix jacchus).

Scientific reports·2026
Same journal

Role and mechanism of AOPPs-induced NOX4-mediated ferroptosis in intervertebral disc degeneration.

Scientific reports·2026
查看所有相关文章

相关实验视频

Updated: Jul 11, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

贝叶斯式AEWMA控制图在排序集采样下与应用到可靠性工程的应用.

Imad Khan1, Muhammad Noor-Ul-Amin2, Dost Muhammad Khan1

  • 1Department of Statistics, Abdul Wali Khan University Mardan, Mardan, Pakistan.

Scientific reports
|November 17, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的贝叶斯AEWMA控制图,使用各种损失函数和排序集采样 (RSS) 来改进过程平均转移检测. 拟议的图表显示了比现有方法更高的性能,特别是RSS设计.

更多相关视频

Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.2K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

920

相关实验视频

Last Updated: Jul 11, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K
Design and Optimization Strategies of a High-Performance Vented Box
14:23

Design and Optimization Strategies of a High-Performance Vented Box

Published on: June 9, 2023

1.2K
Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

920

科学领域:

  • 统计过程控制 统计过程控制
  • 质量工程 质量工程
  • 贝叶斯的推理是贝叶斯的推理.

背景情况:

  • 传统的控制图表难以检测微小的过程转移.
  • 整合不同的损失功能和采样设计可以提高灵敏度.
  • 贝叶斯式方法为不确定性量化提供了一个强大的框架.

研究的目的:

  • 开发一个新的贝叶斯指数加权移动平均线 (AEWMA) 控制图.
  • 为了结合多个损失函数 (例如,平方误差,Linex) 和排序集采样 (RSS) 设计.
  • 改进检测过程中中等的小到中等变化的检测.

主要方法:

  • 实施贝叶斯式AEWMA控制图与信息先验.
  • 用各种损失函数用于后置和后置预测分布.
  • 应用各种排序集采样 (RSS) 方案,并与简单随机抽样 (SRS) 进行比较.
  • 使用平均运行长度 (ARL) 和运行长度标准偏差 (SDRL) 的性能评估.
  • 蒙特卡洛模拟和半导体制造中的案例研究 (硬过程).

主要成果:

  • 拟议的贝叶斯式AEWMA控制图与集成的损失函数和RSS设计显著超过现有图表.
  • 排列采集采样 (RSS) 设计在检测过程平均转移方面表现出优异的性能,相比于简单的随机采样 (SRS).
  • 该图表有效地检测到小到中等的变化,在现实应用中显示出更高的灵敏度.

结论:

  • 开发的贝叶斯AEWMA控制图为过程平均值转移检测提供了更精确,更有效的方法.
  • 整合多个损失函数和RSS方案提高了图表识别失控信号的能力.
  • 这种方法为制造过程的质量改进提供了有价值的工具.