Exploratory data analysis: Difference between revisions

Revision as of 06:41, 19 March 2023

Revision as of 09:27, 24 August 2023

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Tukey defined data analysis in 1961 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."<ref>[http://projecteuclid.org/download/pdf_1/euclid.aoms/1177704711 John Tukey-The Future of Data Analysis-July 1961]</ref>

Exploratory data analysis is an analysis technique to analyze and investigate the data set and summaries the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis. This report will be falling light on organizational management with the different factors like physical, cognitive and emotional. This factors have been judged by different predictive factors like environmental threat, inability to find respite and affective trust and cognitive trust.

Tukey's championing of EDA encouraged the development of [[Computational statistics|statistical computing]] packages, especially [[S (programming language)|S]] at [[Bell Labs]].<ref>{{Citation |last=Becker |first=Richard A. |title=A Brief History of S |publisher=AT&T Bell Laboratories |place=Murray Hill, New Jersey |access-date=2015-07-23 |url=http://www2.research.att.com/areas/stat/doc/94.11.ps |format=PS |archive-url=https://web.archive.org/web/20150723044213/http://www2.research.att.com/areas/stat/doc/94.11.ps |archive-date=2015-07-23 |quotation="... we wanted to be able to interact with our data, using Exploratory Data Analysis (Tukey, 1971) techniques."}}</ref> The S programming language inspired the systems [[S-PLUS]] and [[R (programming language)|R]]. This family of statistical-computing environments featured vastly improved dynamic visualization capabilities, which allowed statisticians to identify [[outlier]]s, [[trend estimation|trends]] and [[pattern recognition|patterns]] in data that merited further study.