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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty: Confidence Intervals00:54

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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 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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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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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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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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视觉化集合中的不确定性

Christian Tominski, Michael Behrisch, Susanne Bleisch

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    想象不确定的数据集是具有挑战性的. 本研究引入了一个框架,通过考虑集合成员身份,属性和不确定性类型,将不确定性整合到集合可视化中.

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    科学领域:

    • 信息可视化 信息可视化
    • 数据分析 数据分析
    • 人与计算机的交互

    背景情况:

    • 集合可视化对于分析集合类型数据至关重要.
    • 在集合可视化中描绘不确定性仍然是一个开放的研究挑战.
    • 现有的方法缺乏系统的方法来整合不确定性.

    研究的目的:

    • 确定受不确定性影响的数据集的各个方面.
    • 确定不确定性特征如何影响可视化设计.
    • 开发一个可视化数据集不确定性的框架.

    主要方法:

    • 开发了一个概念框架,将集合数据属性 (成员,属性) 与不确定性类别 (确定性,未定义,定义) 联系起来.
    • 系统地分析基于框架的基本可视化示例.
    • 综合了关于不确定性可视化的现有知识.

    主要成果:

    • 确定了不确定性影响的数据集的关键方面.
    • 分类不确定性类型与设置可视化相关.
    • 提供了将不确定性整合到集合可视化中的基础示例.

    结论:

    • 结构化框架对于有效可视化数据集中的不确定性至关重要.
    • 了解数据方面和不确定性类型之间的相互作用指导可视化设计.
    • 进一步的研究可以建立在这个框架上,以创建强大的不确定性意识集可视化.