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Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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...
Bar Graph01:07

Bar Graph

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...
Multiple Bar Graph01:07

Multiple Bar Graph

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...
Review and Preview01:13

Review and Preview

Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
Review and Preview01:10

Review and Preview

In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...

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Updated: Jun 28, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Improved techniques for quantitatively comparing data visualizations.

David S Pieczkiewicz1, David Sean Pieczkiewicz, Luke V Rasmussen

  • 1Biomedical Informatics Research Center, Marshfield Clinic, Marshfield, WI, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|November 13, 2008
PubMed
Summary
This summary is machine-generated.

Researchers can now compare data visualization performance using advanced statistical methods. These techniques, including receiver operating characteristic curve analysis and generalized linear mixed models, enhance the quantitative evaluation of visualization accuracy and decision speed.

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Area of Science:

  • Data Visualization
  • Statistical Analysis
  • Human-Computer Interaction

Background:

  • Data visualizations are crucial for managing large datasets.
  • Comparing the effectiveness of different visualizations is a common research challenge.
  • Existing methods for evaluating visualization performance may be insufficient.

Purpose of the Study:

  • To introduce and advocate for advanced statistical techniques for comparing data visualizations.
  • To provide methods for evaluating both accuracy and speed of user decisions based on visualizations.

Main Methods:

  • Multiple-reader multiple-case receiver operating characteristic (ROC) curve analysis.
  • Generalized linear mixed models (GLMMs).
  • Quantitative evaluation of decision-making performance with visual data.

Main Results:

  • These statistical methods offer advantages over simpler evaluation strategies.
  • The proposed techniques enable a more robust comparison of visualization performance.
  • Accuracy and decision speed can be effectively measured and compared.

Conclusions:

  • Multiple-reader multiple-case ROC analysis and GLMMs should be integrated into the quantitative evaluation of data visualizations.
  • These methods provide a rigorous framework for assessing visualization effectiveness.
  • Adoption of these techniques will improve the scientific rigor in data visualization research.