Exploratory Data Analysis

Exploratory data analysis (EDA) seeks to learn general patterns or tendencies in data and find specific occurrences that deviate from the general patterns. John Tukey makes a clear distinction between confirmatory data analysis, where one draws inferential conclusions, and exploratory methods, where one places few assumptions on the distributional shape of the data and looks for interesting patterns. This chapter focus on some fundamental exploratory methods described by Tukey including batch comparison, relationships of two variables, patterns in time series and exploring a batch of fractions. There are four general themes of exploratory data analysis, namely Revelation, Resistance, Residuals, and Reexpression, collectively called the four R’s. The four R’s are illustrated for the basic exploratory methods with suitable graphical methods to gain insight into the data.