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Exploratory Data Analysis

  • Reference work entry
Encyclopedia of Database Systems

Definition

Exploratory Data Analysis (EDA) is an approach to data analysis that employs a number of different techniques to:

  1. 1.

    Look at data to see what it seems to say,

  2. 2.

    Uncover underlying structures,

  3. 3.

    Isolate important variables,

  4. 4.

    Detect outliers and other anomalies,

  5. 5.

    Suggest suitable models for conventional statistics.

Key Points

The term “Exploratory Data Analysis” was introduced by John W. Tukey who in [2] shows how simple graphical and quantitative techniques can be used to open-mindedly explore data.

Typical graphical techniques are

  1. 1.

    Plotting the raw data (e.g., stem-and-leaf diagrams, histograms, scatter plots)

  2. 2.

    Plotting simple statistics (e.g., mean plots, box plots, residual plots)

  3. 3.

    Positioning (multiple) plots to amplify cognition

Typical quantitative techniques are

  1. 1.

    Interval estimation

  2. 2.

    Measures of location or of scale

  3. 3.

    Shapes of distributions

Exploratory data analysis can help to improve the results of statistical hypothesis testing by forcing one to...

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Recommended Reading

  1. Berry M.J.A. and Linoff G.S. Mastering Data Mining. Wiley, New York, 2000.

    Google Scholar 

  2. Tukey J.W. Exploratory Data Analysis. Addison Wesley, Reading, MA, 1977.

    MATH  Google Scholar 

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Hinterberger, H. (2009). Exploratory Data Analysis. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1384

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