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Related Concept Videos

Boxplot01:12

Boxplot

14.6K
Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
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Modified Boxplots00:57

Modified Boxplots

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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...
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Microsoft Excel: Median, Quartile range, and Box Plots01:29

Microsoft Excel: Median, Quartile range, and Box Plots

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In Microsoft Excel, calculating the median, interquartile range, and creating box plots can help understand the distribution of your data.
Median and Quartile Range: The median is calculated using the formula `=MEDIAN(range)', which provides the middle value of your data set. Quartiles divide your data into four equal parts. To find the first and third quartiles, use ‘=QUARTILE(range, 1)' and ‘=QUARTILE(range, 3)', respectively. The interquartile range (IQR), which...
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5-Number Summary01:04

5-Number Summary

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In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
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Multiple Bar Graph01:07

Multiple Bar Graph

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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...
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Scatter Plot01:15

Scatter Plot

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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:
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Related Experiment Video

Updated: Apr 7, 2026

Conditions Affecting Social Space in Drosophila melanogaster
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Conditions Affecting Social Space in Drosophila melanogaster

Published on: November 5, 2015

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Surface boxplots.

Marc G Genton1, Christopher Johnson2, Kristin Potter3

  • 1CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.

Stat (International Statistical Institute)
|July 3, 2015
PubMed
Summary
This summary is machine-generated.

We introduce a surface boxplot for image analysis, using volume depth to order and identify outlier images. This visualization tool aids in exploring image datasets like brain scans and climate models.

Keywords:
band depthfast algorithmfunctional boxplotimage datalarge datasetrankingvisualizationvolume depth

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Area of Science:

  • Computer Vision
  • Data Visualization
  • Image Analysis

Background:

  • Exploratory data analysis of image datasets requires robust visualization tools.
  • Existing methods may not effectively capture the complexity of high-dimensional image data.

Purpose of the Study:

  • Introduce a novel surface boxplot for visualizing and analyzing image samples.
  • Develop a method for ordering images based on volume depth and identifying outliers.
  • Create a graphical tool for interactive exploration of image data characteristics.

Main Methods:

  • Utilize volume depth to define an ordering of images treated as surfaces.
  • Implement an exact and fast algorithm for ranking images.
  • Construct a graphical interface to display the surface boxplot and volume depth distributions.
  • Apply the surface boxplot to brain imaging and climate model output datasets.

Main Results:

  • The surface boxplot effectively visualizes image sample characteristics.
  • Volume depth ranking successfully identifies median and outlying images.
  • The graphical tool facilitates the detection of interesting features in outlier images.
  • The method is applicable to diverse image datasets, including medical and scientific imaging.

Conclusions:

  • The surface boxplot is a valuable tool for exploratory image analysis.
  • Volume depth provides a robust measure for image ordering and outlier detection.
  • The developed visualization method enhances the understanding of image dataset variations.