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相关概念视频

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

32.0K
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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Ordinal Level of Measurement00:55

Ordinal Level of Measurement

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.5K
Histogram01:05

Histogram

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
12.8K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
28.0K
Aggregates Classification01:29

Aggregates Classification

310
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
310

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相关实验视频

Updated: Jun 16, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

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将多维值扩展到包括分类属性.

Jennifer A Whitty1, Nicolas Krucien2, Caitlin Thomas2

  • 1Evidera, London UK; Norwich Medical School, University of East Anglia, Norwich UK; School of Pharmacy, University of Queensland, Brisbane, Australia.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
|June 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新方法,将分类属性纳入多维值 (MDT) 模型. 该方法允许分类数据与连续变量一起以最小的精度损失,扩展MDT应用.

关键词:
多维值设置多维值设置分析 分析 分析分类属性 分类属性 分类属性.设计 设计 设计 设计偏好诱导的诱导方式

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相关实验视频

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科学领域:

  • 决策科学 决策科学
  • 营销科学 营销科学
  • 心理测量 心理测量 心理测量

背景情况:

  • 多维值 (MDT) 是一种很有价值的工具,可以引起个人偏好.
  • 当前的MDT模型假定连续属性,限制它们的适用性与分类数据.

研究的目的:

  • 提出一个新的框架,将分类属性纳入MDT.
  • 概述一个设计MDT研究的过程,该过程中混合了分类和连续属性.

主要方法:

  • 分类属性排名和分数的分数分配.
  • 排名属性规模波动的重要性.
  • 对于连续属性的值练习.
  • 交易分类属性波动与连续属性波动对比.

主要成果:

  • 在MDT中包含分类属性是可行的,但精度略有损失.
  • 精度损失取决于分类属性的重要性和排名.
  • 连续属性的数量和选择任务对精度的影响最小.

结论:

  • 拟议的框架成功地将MDT扩展到包括分类属性.
  • 当分类属性不是偏好的主要驱动因素时,这种方法特别有用.
  • 建议在应用环境中进行进一步的研究.