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相关概念视频

Relative Risk01:12

Relative Risk

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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Factors Affecting Perception01:25

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Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
An illustrative example of a perceptual set is the scenario where an airline pilot told...
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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相关实验视频

Updated: May 7, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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基于多功能融合的消费者感知风险预测及其解释性研究.

Lin Qi1,2, Yunjie Xie1,3, Qianqian Zhang1,3

  • 1School of Economics & Management, Beijing Information Science & Technology University, Beijing, China.

PloS one
|January 3, 2025
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概括

这项研究使用审查内容和网站数据预测电子商务的感知风险. 质量,安全和价格等关键特征显著影响用户的风险感知.

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科学领域:

  • 电子商务是一个电子商务.
  • 消费者行为 消费者行为
  • 数据科学数据科学数据科学

背景情况:

  • 电子商务面临着诸如内容均化和高用户感知风险等挑战.
  • 分析在线评论和网站数据对于理解和减轻这些风险至关重要.

研究的目的:

  • 开发电子商务中感知风险的预测模型.
  • 确定影响不同产品类别感知风险的关键特征.

主要方法:

  • 使用KeyBERT-TextCNN从262,752个在线评论中提取主题功能.
  • 结合主题特征与产品/商户数据.
  • 使用PCA-K-medoids-XGBoost进行预测建模.

主要成果:

  • 确定了影响感知风险的11个关键特征.
  • 实现了高性能,精度为84%,回忆率为86%,F1得分为85%.
  • 质量,功能和价格对于电子产品至关重要;皮肤安全对于护肤至关重要.

结论:

  • 开发的模型有效预测了电子商务中感知到的风险.
  • 功能的重要性因产品类型而异,突出显示了需要特定上下文分析的需要.
  • 高风险样本和正常样本之间存在显著差异,为风险缓解策略提供了信息.