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Scatter Plot01:15

Scatter Plot

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:
Modified Boxplots00:57

Modified Boxplots

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...
Residual Plots01:07

Residual Plots

A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Boxplot01:12

Boxplot

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...
Interpreting R Charts01:22

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...

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Related Experiment Video

Updated: May 9, 2026

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
08:25

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

Published on: December 6, 2024

Splatterplots: overcoming overdraw in scatter plots.

Adrian Mayorga1, Michael Gleicher

  • 1Department of Computer Sciences, University of Wisconsin-Madison, 1210 West Dayton Street, Madison, WI 53706, USA. adrm@cs.wisc.edu

IEEE Transactions on Visualization and Computer Graphics
|July 13, 2013
PubMed
Summary
This summary is machine-generated.

Splatterplots offer a new way to visualize dense scatter data, overcoming limitations of traditional plots. This novel technique effectively displays millions of data points, revealing trends and outliers.

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Measuring the Behavioral Effects of Intraocular Scatter
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Measuring the Behavioral Effects of Intraocular Scatter

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Measuring the Behavioral Effects of Intraocular Scatter
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Measuring the Behavioral Effects of Intraocular Scatter

Published on: February 18, 2021

Area of Science:

  • Data Visualization
  • Computer Graphics
  • Information Visualization

Background:

  • Traditional scatter plots struggle with overdraw as data density increases, obscuring patterns and outliers.
  • Overplotting in scatter plots hinders the analysis of data distributions and subgroup relationships.

Purpose of the Study:

  • To introduce Splatterplots, a novel data visualization technique designed to overcome the limitations of traditional scatter plots.
  • To enable effective visualization of massive datasets with high point density.

Main Methods:

  • Splatterplots abstract data density, bounding points per screen unit while allowing continuous zoom.
  • Techniques include automatic grouping of dense data into contours and sampling of remaining points.
  • Perceptually based color blending is used to differentiate data subgroups.

Main Results:

  • Visualizations represent dense data regions as smooth shapes and explicitly show outliers.
  • The GPU is leveraged for efficient computation and rendering of Splatterplots.
  • The method scales to millions of points, enabling interaction with massive datasets.

Conclusions:

  • Splatterplots provide an effective alternative to traditional scatter plots for visualizing dense datasets.
  • This technique enhances communication of data trends, outliers, and relationships in large-scale data.
  • Splatterplots enable scalable scatter plot visualizations for millions of data points.