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相关概念视频

Bar Graph01:07

Bar Graph

16.4K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
16.4K
Histogram01:05

Histogram

13.0K
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...
13.0K
Multiple Bar Graph01:07

Multiple Bar Graph

5.1K
As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
5.1K
Probability Histograms01:17

Probability Histograms

11.4K
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.
11.4K
Modified Boxplots00:57

Modified Boxplots

9.6K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
9.6K
Microsoft Excel: Plotting Mean, SD, and SE01:18

Microsoft Excel: Plotting Mean, SD, and SE

213
In Microsoft Excel, plotting the mean along with standard deviation (SD) and standard error (SE) helps visualize data variability and reliability. To plot these values, follow these steps:
First, calculate the mean, SD, and SE of your data. The mean is obtained using the formula `=AVERAGE(range)`, while SD can be calculated with `=STDEV.P(range)` for a population or `=STDEV.S(range)` for a sample. SE is calculated as `=SD/SQRT(n)`, where `n` is the sample size.
To plot these values, use a bar...
213

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相关实验视频

Updated: Jun 28, 2025

Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression
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Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression

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海洋堆积图:用直方图取代条形图.

Alice Dorothy Stuart1, Maja Ilić2, Benno I Simmons3

  • 1School of Environmental Sciences University of East Anglia, Norwich Research Park Norwich UK.

Ecology and evolution
|April 18, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新型数据可视化工具 - - 海堆图,以准确地表示大型数据集. 这种新方法解决了现有地块类型的局限性,改善了科学研究中的数据理解.

关键词:
条形图表 条形图表数据分布数据的分布.数据可视化数据可视化基因组图 (Histogram) 是指一个基因组图.总结统计的总结统计.

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Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

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科学领域:

  • 数据可视化 数据可视化
  • 统计图形 统计图形
  • 科学沟通科学沟通

背景情况:

  • 图表可以提高数据的理解力,但设计不佳可能会误导读者.
  • 现有的图形类型 (例如,盒子图形,密度图形) 具有大或不均分布的数据集的限制.
  • 像条形图和点和胡须图这样没有信息的图表在生态和保护文献中很普遍.

研究的目的:

  • 引入一种新的图形类型,即海堆图,以准确有效地表示大型单变量数据集.
  • 为了比较海堆地图与常用的地图类型的有效性.
  • 分析生态和保护期刊中的当前数据可视化实践.

主要方法:

  • 用于数据分布表示的五种常见图形类型 (点和胡须,框图,密度,单变散,点图) 的比较.
  • 分析了四个生态和保护期刊中的数据,以确定流行的可视化方法.
  • 开发海上堆图和附带的R包 ("海上堆图").

主要成果:

  • 通常评估的图形类型在较大的样本大小下难以读取,或可能误导数据分布.
  • 条形图和点和胡须图构成了60%的单变量数据面板用于分析期刊的比较.
  • 16%的面板结合了图形类型 (例如,带有密度图形的盒图) 来改善数据显示,这表明需要更好的可视化工具.

结论:

  • 海洋堆图提供了一种改进的方法来可视化大型和不均分布的单变量数据,克服现有图的局限性.
  • 开发用户友好和准确的图形类型,如海图形,是满足科学领域有效数据可视化需求的必要条件.
  • "海图" R 包为研究人员提供了一种工具,可以实现这种新的可视化技术.