Difference between revisions of "Exploratory data analysis"
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Latest revision as of 10:41, 31 August 2016
In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods.
Description
A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.
Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments.
EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.
See also
- Anscombe's quartet, on importance of exploration
- Configural frequency analysis
- Data dredging
- Descriptive statistics
- Initial data analysis
- Predictive analytics
- Statistical graphics
- Structured data analysis (statistics)
External links
- Exploratory data analysis @ Wikipedia.org
