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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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Interpreting R Charts01:22

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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...
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Relative Frequency Histogram01:14

Relative Frequency Histogram

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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F Distribution01:19

F Distribution

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The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...
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Interpreting X̄ Charts01:13

Interpreting X̄ Charts

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Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
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Related Experiment Video

Updated: Jun 25, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
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MAD-FC: A fold change visualization with readability, proportionality, and symmetry.

Bruce A Corliss1,2, Yaotian Wang3, Francis P Driscoll1

  • 1School of Data Science, University of Virginia, Charlottesville, Virginia, United States of America.

Plos One
|May 31, 2024
PubMed
Summary

We introduce a new fold change visualization method, mirrored axis distortion of fold change (MAD-FC), that improves upon linear and log plots. MAD-FC offers better readability, proportionality, and symmetry for fold change data visualization.

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Area of Science:

  • Data Visualization
  • Bioinformatics
  • Statistical Graphics

Background:

  • Effective visualization of fold change is crucial in data analysis, particularly in biomedical research.
  • Existing linear and log transformations of fold change data present limitations in terms of readability, proportionality, symmetry, and dynamic range.

Purpose of the Study:

  • To propose a novel fold change transformation that combines desirable visualization properties.
  • To address the shortcomings of linear and log plots for fold change visualization.

Main Methods:

  • Introduction of the mirrored axis distortion of fold change (MAD-FC) transformation.
  • Evaluation of MAD-FC properties including readability, proportionality, symmetry, and dynamic range.
  • Application and illustration of MAD-FC using biomedical data.

Main Results:

  • MAD-FC extends linear visualization to achieve readability, proportionality, and symmetry.
  • Linear plots offer readability and partial proportionality but lack high dynamic range and symmetry.
  • Log plots provide high dynamic range and symmetry but sacrifice proportionality.

Conclusions:

  • MAD-FC offers a valuable alternative for visualizing fold change data, especially when high dynamic range is not a primary requirement.
  • The proposed MAD plots may be more suitable than traditional log or linear plots for specific applications in data analysis.