Exploratory data analysis

By K. Smith‐Miles. International Encyclopedia of Statistical Science, 2018

8,832 citations recorded by the source corpus.

Exploratory data analysis, or EDA for short, is a term coined by John W. Tukey for describing the act of looking at data to see what it seems to say. This article gives a description of some typical EDA procedures and discusses some of the principles of EDA. Copyright © 2009 John Wiley & Sons, Inc.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/5bb58f12eb22b87a70cd759a0cd28bae7eb7011d

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Citations in the corpus

Visualization analysis & design (2015, neutral): “Boxplots The boxplot was originally proposed by Tukey and popularized through his influential book on Exploratory Data Analysis [Tukey 77].”
Spatial data mining and geographic knowledge discovery - An introduction (2009, methodology): “Geovisualization also has close relations with exploratory data analysis (EDA) and exploratory spatial data analysis (ESDA) (Anselin , 1999; Bailey & Gatrell, 1995; Tukey, 1977), which links statistical graphics and maps and relies on the human expert to interact with data, visually identify pattern”
Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets (2017, methodology): “The box-and-whisker technique was first proposed by Tukey [35] to show the distribution of data, examine its symmetry, and indicate outliers.”
Modeling heterogeneous treatment effects in large-scale experiments using Bayesian Additive Regression Trees (2010, methodology): “Finally, in order to account for the testing of a potentially large number of candidate treatmentcovariate interactions, we clearly distinguish between an exploratory data analysis stage and a confirmatory stage (Tukey 1977).”
The Primary Factors that Characterize Effective Interventions with Juvenile Offenders: A Meta-Analytic Overview (2009, methodology): “5 times the interquartile range beyond the 25th and 75th percentiles (Tukey, 1977) were recoded to the respective inner fence values.”
A Generalized Unsharp Masking Algorithm (2011, methodology): “In Tukey's own words the data model is: " PLUS residuals " ([28] pp.208).”
Mastering the Information Age - Solving Problems with Visual Analytics (2010, background): “One of the most important steps in Early visual analytics: exploratory data analysis this direction was the need to move from confirmatory data analysis (using charts and other visual representations to just present results) to exploratory data analysis (interacting with the data/results), which was”
Effect Size in Single-Case Research: A Review of Nine Nonoverlap Techniques (2011, background): “…as the proportion of Phase B data that are above a median slope plotted from Phase A data, but then extended into Phase B. White and Haring (1980) hand fit a “split middle” median line to Phase A data, but any other trend line, for example, a Tukey tri-split line (Tukey, 1977), would work as well.”
Determining operational capital at risk: an empirical application to the retail banking (2009, neutral): “6 See Tukey (1977) and Hoaglin et al. (1983 and 1985).”
Beyond Power Calculations (2014, neutral): “First, it is indeed preferable to do a design analysis ahead of time, but a researcher can analyze data in many different ways—indeed, an important part of data analysis is the discovery of unanticipated patterns (Tukey, 1977) so that it is unreasonable to suppose that all potential analyses could h”
AlphaFill: enriching AlphaFold models with ligands and cofactors (2022, methodology): “We use the values of the local r.m.s.d. exceeding the third quartile plus 1.5 times the interquartile range 14 for all transplants with sequence identity of 70% or higher (0.92 Å) and for all trans-plants (3.10 Å) to annotate all AlphaFill transplants as ‘medium confidence’ and ‘low confidence’,…”
Elaboration and consequences of anchored estimates: An attitudinal perspective on numerical anchoring (2008, positive): “To minimize the disproportionate impact of outliers on the standard scores, we used an outlier procedure developed by Tukey (1977) before calculating the standard scores.”
Examining Social Class and Work Meaning Within the Psychology of Working Framework (2014, methodology): “To normalize education and income, we conducted a series of transformations (square root, logarithmic base 10, and inverse) on each of the variables and chose the least powerful transformation that most effectively normalized the individual variable (Tukey, 1977).”