# Understanding robust and exploratory data analysis pdf

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In statistics , exploratory data analysis EDA is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. 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 , [1] 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. Tukey defined data analysis in as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of mathematical statistics which apply to analyzing data. This family of statistical-computing environments featured vastly improved dynamic visualization capabilities, which allowed statisticians to identify outliers , trends and patterns in data that merited further study. Tukey's EDA was related to two other developments in statistical theory : robust statistics and nonparametric statistics , both of which tried to reduce the sensitivity of statistical inferences to errors in formulating statistical models.
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## 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.

Originally published in hardcover in , this book is now offered in a Wiley Classics Library edition. A contributed volume, edited by some of the preeminent .
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## 3 editions of this work

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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.