Jove
Visualize
联系我们

相关概念视频

Scatter Plot01:15

Scatter Plot

7.1K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
7.1K
Density00:56

Density

14.8K
Density is an important characteristic of substances, crucial in determining whether an object sinks or floats in a fluid. Its SI unit is kg/m3, and its cgs unit is g/cm3. The density of an object helps in identifying its composition, and also reveals information about the phase of the matter and its substructure. The densities of liquids and solids are roughly comparable, consistent with the fact that their atoms are in close contact. However, gases have much lower densities than liquids and...
14.8K
Variability: Analysis01:11

Variability: Analysis

162
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
162
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

228
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
228
Relative Frequency Histogram01:14

Relative Frequency Histogram

5.5K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
5.5K
Probability Histograms01:17

Probability Histograms

11.8K
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.
11.8K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Tracking affordances requires a sophisticated model of life stages, phases, and transitions.

The Behavioral and brain sciences·2026
Same author

Understanding and transcending "p" requires a functional model of psychopathology: Commentary on Caspi et al. (2026).

Journal of psychopathology and clinical science·2026
Same author

It's bigger on the inside: mapping the black box of motivation.

The Behavioral and brain sciences·2025
Same author

How Well Do Bibliometric Indicators Correlate With Scientific Eminence? A Comment on Simonton (2016).

Perspectives on psychological science : a journal of the Association for Psychological Science·2019
Same author

Cognitive gadgets: A provocative but flawed manifesto.

The Behavioral and brain sciences·2019
Same author

Global sex differences in personality: Replication with an open online dataset.

Journal of personality·2019
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jul 25, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

相对密度云:可视化和探索群体差异的多变量模式.

Marco Del Giudice1

  • 1Department of Psychology, University of New Mexico, Albuquerque, New Mexico, United States of America.

PloS one
|June 27, 2023
PubMed
概括

本研究介绍了相对密度云,这是一种用于比较多变量数据中的两个组的新可视化方法. 这种技术使用k-最近邻居密度估计来揭示整个数据分布中的群体差异.

科学领域:

  • 统计 统计 统计 统计
  • 数据可视化 数据可视化
  • 多变量分析多变量分析

背景情况:

  • 现有的相对分布方法对于单变量分析是有效的.
  • 多变量组比较往往缺乏直观的可视化工具.
  • 了解复杂的群体差异需要先进的分析方法.

研究的目的:

  • 引入相对密度云用于多变量组比较.
  • 提供一种可视化和分解组差异的方法.
  • 提高多变量数据分析的解释性.

主要方法:

  • 使用 k-最近邻居 (KNN) 密度估计.
  • 想象在多变量空间中两个群体的相对密度.
  • 将群体差异分解为位置,规模和协变组件.

主要成果:

  • 相对密度云有效地显示整个数据分布中的组差异.
  • 该方法成功地将整体差异分解为可解释的组件.
  • 为实际应用提供了一个可访问的R函数.

结论:

  • 相对密度云为多变量数据分析提供了强大且易于使用的工具.

更多相关视频

Rapid Analysis and Exploration of Fluorescence Microscopy Images
11:41

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

12.4K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K

相关实验视频

Last Updated: Jul 25, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Rapid Analysis and Exploration of Fluorescence Microscopy Images
11:41

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

12.4K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.6K
  • 这种方法有助于探索和理解群体差异的复杂模式.
  • 视觉化增强了分组差异的分解到位置,规模和协变效应.