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Related Concept Videos

Bar Graph01:07

Bar Graph

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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...
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Multiple Bar Graph01:07

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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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Histogram01:05

Histogram

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The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

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The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
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Measurement of <math></math> production with the hadronically decaying boson reconstructed as one or two jets in <i>pp</i> collisions at <math> </math> with ATLAS, and constraints on anomalous gauge couplings.

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Related Experiment Video

Updated: Apr 27, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Information graphs for binary predictors.

G Hughes, N McRoberts, F J Burnett

    Phytopathology
    |July 2, 2014
    PubMed
    Summary

    Binary predictors enhance crop disease risk assessment by providing diagnostic information. This study visualizes information theory concepts for better understanding of disease probability updates in crop protection.

    Area of Science:

    • Agricultural Science
    • Information Theory
    • Biostatistics

    Background:

    • Binary predictors are crucial for risk factor analysis in crop protection decision-making.
    • Bayesian updating of crop disease probabilities relies on evidence from these predictors.
    • Receiver operating characteristic curves offer diagrammatic interpretation of diagnostic probabilities.

    Purpose of the Study:

    • To analyze binary predictors through the lens of diagnostic information theory.
    • To provide diagrammatic interpretations of key information theoretic measures.
    • To illustrate the relationship between diagnostic information and probabilities.

    Main Methods:

    • Introduction to entropy and expected mutual information.
    • Application of information theoretic concepts to an example data set.
    Keywords:
    diagnosisdisease managemententropyinformation theory

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  • Development of information graphs for visualization.

    • Main Results:

    • Diagrammatic interpretations of expected mutual information, relative entropy, information inaccuracy, information updating, and specific information were generated.
    • Information graphs visually represented correspondences between diagnostic information and diagnostic probabilities.
    • The utility of binary predictors in conveying diagnostic information was demonstrated.

    Conclusions:

    • Binary predictors serve as valuable tools for quantifying diagnostic information in crop disease management.
    • Information graphs offer an intuitive method for understanding complex probabilistic relationships.
    • This approach enhances the interpretation of risk factors and disease probability assessments.