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Related Concept Videos

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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Outliers and Influential Points01:08

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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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Time-Series Graph00:54

Time-Series Graph

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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...
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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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Scatter Plot01:15

Scatter Plot

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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:
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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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Related Experiment Video

Updated: Apr 30, 2026

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
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Context-preserving, dynamic word cloud visualization.

Weiwei Cui, Yingcai Wu, Shixia Liu

    IEEE Computer Graphics and Applications
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces dynamic word clouds to visualize how content changes over time. This method groups related terms, aiding exploration of large document sets.

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    Area of Science:

    • Information Visualization
    • Digital Humanities
    • Computational Linguistics

    Background:

    • Analyzing large document collections is challenging.
    • Traditional methods struggle to represent content evolution effectively.
    • Visualizing thematic shifts requires novel approaches.

    Purpose of the Study:

    • To develop a method for illustrating content evolution.
    • To enhance user exploration of large document corpora.
    • To present dynamic changes in textual data visually.

    Main Methods:

    • Generating sequences of context-preserving word clouds.
    • Grouping semantically related words within word clouds.
    • Coupling word cloud sequences with trend charts.

    Main Results:

    • Successfully visualized content evolution through dynamic word clouds.
    • Demonstrated grouping of related terms for clarity.
    • Integrated trend charts to summarize content changes.

    Conclusions:

    • The proposed method offers an intuitive way to explore document content evolution.
    • Dynamic word clouds combined with trend analysis improve understanding of large datasets.
    • This approach facilitates deeper insights into textual data changes over time.