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Related Concept Videos

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:
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...
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...
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...
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...
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: Jun 22, 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

Visualising disease progression on multiple variables with vector plots and path plots.

Stanley E Lazic1, Sarah L Mason, Andrew W Michell

  • 1Cambridge Computational Biology Institute, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK. stan.lazic@cantab.net

BMC Medical Research Methodology
|May 29, 2009
PubMed
Summary
This summary is machine-generated.

New vector and path plots visualize individual disease progression across multiple variables simultaneously. These graphical methods offer advantages over traditional separate variable plotting for exploratory data analysis.

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Data Visualization

Background:

  • Observing individual disease progression over time is crucial.
  • Simultaneous visualization of multiple patient outcomes is advantageous.
  • Current methods often focus on group averages, not individual trajectories.

Purpose of the Study:

  • To introduce novel graphical methods for visualizing individual disease progression.
  • To demonstrate the utility of these methods for exploratory data analysis.
  • To overcome limitations of plotting variables separately.

Main Methods:

  • Development of vector plots and a path plot.
  • Application to data from a longitudinal Huntington's disease study.
  • Incorporation of methods to visualize initial/final values, changes over time, and individual trajectories.

Main Results:

  • Vector and path plots effectively visualize per-patient changes in multiple outcome variables.
  • Disease trajectories and individual paths from initial to final observations are traceable.
  • Categorical and continuous variables can be integrated using color, line types, and separate panels; summary statistics aid interpretation.

Conclusions:

  • Vector and path plots are valuable tools for exploratory data analysis.
  • These methods enable detailed visualization of individual-level, multi-variable longitudinal data.
  • They offer significant advantages over analyzing each variable in isolation.