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Related Concept Videos

Boxplot01:12

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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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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.
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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,...
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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.
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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.
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Flexplot: Graphically-based data analysis.

Dustin Fife1

  • 1Department of Psychology.

Psychological Methods
|November 29, 2021
PubMed
Summary
This summary is machine-generated.

Scientific graphics are underused due to software complexity. The R package flexplot simplifies creating informative visualizations for statistical models, improving data interpretation.

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Area of Science:

  • Data Visualization
  • Scientific Communication
  • Statistical Graphics

Background:

  • The human visual system processes information rapidly, yet scientific literature suffers from a deficit of graphics.
  • Traditional scientific graphics can unintentionally bias data perception.
  • Creating effective scientific graphics is often hindered by complex software and steep learning curves.

Purpose of the Study:

  • To introduce flexplot, an R package designed to simplify the creation of statistical graphics.
  • To automate graphical decision-making and facilitate the visualization of statistical models.
  • To improve the accessibility and accuracy of data visualization in scientific research.

Main Methods:

  • flexplot utilizes a formula-based approach in the R programming language.
  • It offers one-line functions for visualizing various statistical models, from bivariate to multilevel.
  • The package integrates established techniques (e.g., added variable plots) with novel visualization tools (e.g., ghost lines).

Main Results:

  • flexplot significantly reduces the complexity and coding effort required for generating statistical graphs.
  • It enables seamless integration of graphics with statistical modeling procedures.
  • Researchers can produce sophisticated visualizations for diverse models, including regression, ANOVA, and multilevel analyses.

Conclusions:

  • flexplot addresses the underuse and misuse of graphics in scientific literature by simplifying visualization.
  • The package empowers researchers to create accurate and informative graphs, enhancing data interpretation.
  • flexplot promotes better scientific communication through accessible and powerful data visualization tools.