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Related Concept Videos

How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Bar Graph01:07

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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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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Palettailor: Discriminable Colorization for Categorical Data.

Kecheng Lu, Mi Feng, Xin Chen

    IEEE Transactions on Visualization and Computer Graphics
    |October 13, 2020
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    Summary
    This summary is machine-generated.

    This study introduces Palettailor, an automated method for creating and assigning color palettes in data visualizations. Palettailor enhances visual discrimination by considering data characteristics, outperforming existing approaches.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Computational Aesthetics

    Background:

    • Existing methods often separate color palette creation from assignment.
    • This separation can lead to suboptimal visual discrimination of data classes.
    • Data characteristics are crucial for effective color palette design.

    Purpose of the Study:

    • To develop an integrated approach for creating and assigning color palettes tailored to visualization data.
    • To improve the visual discrimination of classes in multi-class scatterplots, line charts, and bar charts.
    • To create an automated system that considers data characteristics for palette generation.

    Main Methods:

    • Utilized a customized optimization algorithm based on simulated annealing.
    • Incorporated three scoring functions: point distinctness, name difference, and color discrimination.
    • Developed Palettailor, a fully-automated approach for color palette generation and assignment.

    Main Results:

    • Palettailor generated color palettes with higher discrimination quality compared to state-of-the-art methods.
    • User studies and case studies validated the effectiveness of the generated palettes.
    • The approach demonstrated efficiency, allowing for user modifications in color selection.

    Conclusions:

    • Palettailor offers a superior, automated solution for data visualization color palette design.
    • The integrated approach enhances visual discrimination and user experience.
    • The method is adaptable and efficient for practical applications in data visualization.