Understanding robust and exploratory data analysis pdf
Exploratory data analysis
In this chapter, the reader will learn about the most common tools available for exploring a dataset, which is essential in order to gain a good understanding of the features and potential issues of a dataset, as well as helping in hypothesis generation. Exploratory data analysis EDA is an essential step in any research analysis. The primary aim with exploratory analysis is to examine the data for distribution, outliers and anomalies to direct specific testing of your hypothesis. It also provides tools for hypothesis generation by visualizing and understanding the data usually through graphical representation [ 1 ]. EDA aims to assist the natural patterns recognition of the analyst. Finally, feature selection techniques often fall into EDA.
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This paper introduces the family of techniques called exploratory data analysis. Unlike classical confirmatory statistics which rely upon strict distributional assumptions, parameter estimation, and hypothesis testing, EDA adopts an informal method of data examination designed to explore the structure of the data. Three representative EDA techniques are introduced and applications to marketing data sets are presented. Unable to display preview. Download preview PDF. Skip to main content.
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