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1.3.5: Graphs of Numeric Data

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    28660
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    Numeric data is treated differently from categorical data as there exists quantifiable differences in the data values. In analyzing quantitative data, we can describe quantifiable features such as the center, the spread, the shape or skewness18, and any unusual features (such as outliers).  

    Interpreting and Describing Numeric Data

    Center – Where is the middle of the data, what value would represent the average or typical value?

    Spread‐ How much variability is there in the data? What is range of the data? (range is highest value – lowest value.)

    Shape – Are the data values symmetric or is it skewed positive or negative?  Are the values clustered toward the center, evenly spread, or clustered towards the extreme values?

    clipboard_e97eb115e486df02cfd9ab3414d70bc27.png

    Unusual Features – Are there outliers (values that are far removed from the bulk of the data?)

    Example: Students browsing the web

    This data represents how much time 30 students spent on a web browser (on the Internet) in a 24 hour period.19

    Data is rounded to the nearest minute.

    clipboard_ed166aa757f308aaf6164da0076a7adb7.png    clipboard_ec3372b9abe4a31165bd618160d180632.png

     

    This data set is continuous, ratio, quantitative data, even though times are rounded to the nearest integer. Sample data presented unsorted in this format are sometimes called raw data.

    Not much can be understood by looking simply at raw data, so we want to make appropriate graphs to help us conduct preliminary analysis.


    1.3.5: Graphs of Numeric Data is shared under a CC BY-SA license and was authored, remixed, and/or curated by LibreTexts.

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