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相关概念视频

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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Contingency Table01:29

Contingency Table

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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
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Updated: May 26, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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对于分类数据的贝叶斯式Gower协议.

John Hughes1

  • 1Lehigh University, Bethlehem, PA, 18015, USA. drjphughesjr@gmail.com.

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|February 24, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了基于Gower距离的新方法,用于在名义和顺序数据中测量评价者之间的一致性. 这些直观的技术很容易识别有影响力的单位或程序员,并由开源R包支持.

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科学领域:

  • 统计 统计 统计 统计
  • 数据分析 数据分析
  • 测量科学 测量科学 测量科学

背景情况:

  • 准确测量协议在各种领域至关重要,包括医学研究和诊断.
  • 评估名义和顺序数据一致性的现有方法可能是复杂的或范围有限的.
  • 识别有影响力的单位或编码人员对于数据质量评估至关重要.

研究的目的:

  • 开发和介绍新的,简单的,直观的方法来测量名义和顺序数据的一致性.
  • 扩展这些方法以适应单向和双向随机抽样设计.
  • 为协议措施提供贝叶斯推理的强有力的方法.

主要方法:

  • 使用Gower类型距离来计算协议分数.
  • 开发单向和双向随机抽样设计的贝叶斯推理方法.
  • 将拟议的方法应用于模拟和现实世界的数据集,包括放射学和精神病学研究.

主要成果:

  • 提出的基于Gower距离的方法被证明是简单,直观和计算效率高的.
  • 这些方法有助于直接识别有影响力的单位和/或编码人员.
  • 贝叶斯推理框架已成功开发用于单向和双向设计.
  • 这项研究表明,高斯的相互信息作为一个潜在的更有用的协议规模.

结论:

  • 本文所介绍的方法提供了一种灵活且易于使用的方法来量化分级协议.
  • 开源R包"goweragreement"支持这些统计技术的实际应用.
  • 进一步研究替代协议规模,如高斯式相互信息,是有必要的.