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相关概念视频

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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相关实验视频

Updated: Jul 2, 2025

An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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模拟错误信息在有限信心模型中传播:一个模拟研究

Yujia Wu1, Peng Guo1

  • 1School of Management, Northwestern Polytechnical University, Xi'an 710021, China.

Entropy (Basel, Switzerland)
|February 23, 2024
PubMed
概括
此摘要是机器生成的。

这项研究揭示了错误信息如何使用扩展边界信任模型传播. 当与各种离线邻居互动并使用贝叶斯分析时,代理商的错误信息较少,而群众追随者则放大了错误信息.

关键词:
有界的信心,有界的信心.不同质性的异质性错误的信息 错误的信息意见的动态 意见的动态小世界网络的小世界网络.

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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相关实验视频

Last Updated: Jul 2, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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科学领域:

  • 社会科学 社会科学 社会科学
  • 计算机科学 计算机科学
  • 信息科学 信息科学 信息科学

背景情况:

  • 错误信息在整个社会领域构成重大威胁.
  • 了解个人错误信息的机制是一个关键的研究领域.

研究的目的:

  • 在扩展的边界信任模型中调查代理错误信息的范围和机制.
  • 分析不同网络结构和代理行为对错误信息传播的影响.

主要方法:

  • 利用了一个扩展的边界信任模型,将在线选择性邻居和离线邻居纳入Watts-Strogatz小世界网络.
  • 引入并模拟了两种类型的认识论上不负责任的代理人:错误信息传播者和愚蠢的群众追随者.
  • 纳入贝叶斯分析来评估真相发现.

主要成果:

  • 选择性在线网络中更宽的信任区间有助于错误信息的传播.
  • 离线邻居提高了对错误信息的谨慎性.
  • 贝叶斯分析有助于识别真相.
  • 盲目跟随多数的代理人放大了错误信息,并变得更加误导.

结论:

  • 代理互动,网络拓和分析方法显著影响对错误信息的易感性.
  • 知识上不负责任的行为,特别是盲目顺从,加剧了错误信息.
  • 促进多样化的互动和批判性分析的策略对于减轻错误信息至关重要.