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CADDIS Volume 4: Data Analysis

Exploratory Data Analysis

What is Exploratory Data Analysis (EDA)?

Exploratory data analysis (EDA) is an analysis approach that focuses on identifying general patterns in the data, and identifying outliers and features of the data that might not have been anticipated.

EDA is an important first step in any data analysis. Understanding where outliers occur and how different environmental variables are related can help one design statistical analyses that yield meaningful results. In biological monitoring data, sites are likely to be affected by multiple stressors, and so initial explorations of stressor correlations are critical before one attempts to relate stressor variables to biological response variables. Scatterplots and correlation coefficients can provide useful information on the relationships between pairs of variables, but when analyzing numerous variables, basic methods of multivariate visualization can provide greater insights. Mapping data also is critical for understanding the spatial relationship between samples.

EDA can provide insights that may guide efforts to list possible candidate causes.


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