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About 10,000 papers for “Exploratory data analysis”

From Exploratory Data Analysis to Exploratory Spatial Data Analysis

Patricia Abelairas-Etxebarria, Inma Astorkiza · Mathematics and Statistics · 2020

The Exploratory Data Analysis raised by Tuckey [19] has been used in multiple research in many areas but, especially, in the area of the social sciences. This technique searches behavioral patterns of the variables of the study, establishing a hypothesis with the least possible structure. However, in recent times, the inclusion of the spatial perspective in this type of analysis has been revealed as essential because, in many analyses, the observations are spatially autocorrelated and/or they present spatial heterogeneity. The presence of these spatial effects makes necessary to include spatial statistics and spatial tools in the Exploratory Data Analysis. Exploratory Spatial Data Analysis includes a set of techniques that describe and visualize those spatial effects: spatial dependence and spatial heterogeneity. It describes and visualizes spatial distributions, identifies outliers, finds distribution patterns, clusters and hot spots and suggests spatial regimes or other forms of spatial heterogeneity and, it is being increasingly used. With the objective of reviewing the last applications of this technique, this paper, firstly, shows the tools used in Exploratory Spatial Data Analysis and, secondly, reviews the latest Exploratory Spatial Data Analysis applications focused on different areas in the social sciences particularly. As conclusion, it should be noted the growing interest in the use of this spatial technique to analyze different aspects of the social sciences including the spatial dimension.

Cited by 7Open access

Exploratory Data Analysis

Prabhanjan Narayanachar Tattar, Suresh Ramaiah, B.G. Manjunath · 2016

Exploratory data analysis (EDA), developed by John Tukey, is dealt with in this chapter. Many useful graphical methods are also illustrated. EDA summaries, median and further variants, expose the EDA facet and the difference in its approach from that of conventional data summaries. Graphical techniques such as histogram, boxplot, etc. are treated in the EDA sense. Trimean and letter values are demonstrated through examples, and exploratory regression methods of resistant line and median polish conclude the chapter.

Cited by 1

Exploratory Data Analysis

Gerry P. Quinn, Michael J. Keough · Cambridge University Press eBooks · 2023

Applying statistical concepts to biological scenarios, this established textbook continues to be the go-to tool for advanced undergraduates and postgraduates studying biostatistics or experimental design in biology-related areas. Chapters cover linear models, common regression and ANOVA methods, mixed effects models, model selection, and multivariate methods used by biologists, requiring only introductory statistics and basic mathematics. Demystifying statistical concepts with clear, jargon-free explanations, this new edition takes a holistic approach to help students understand the relationship between statistics and experimental design. Each chapter contains further-reading recommendations, and worked examples from today's biological literature. All examples reflect modern settings, methodology and equipment, representing a wide range of biological research areas. These are supported by hands-on online resources including real-world data sets, full R code to help repeat analyses for all worked examples, and additional review questions and exercises for each chapter.

Cited by 0

Exploratory Data Analysis

Concha Bielza, Pedro Larrañaga · Cambridge University Press eBooks · 2020

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.

Cited by 4

Exploratory data analysis

Stephan Morgenthaler · Wiley Interdisciplinary Reviews Computational Statistics · 2009

Abstract 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. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Statistical Graphics and Visualization

Cited by 111Open access

Exploratory Data Analysis

Matthieu Komorowski, Dominic C. Marshall, Justin D. Salciccioli et al. · 2016

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.

Cited by 159Open access

Exploratory Data Analysis

Wiley series in probability and statistics · 2011

The concept of exploratory data analysis (EDA) is generally dismissive of models. In order to understand the highly anti-modeling paradigm of EDA, we need to model EDA itself. In this chapter, the author presents some personal observations which capture much of the essence of EDA. The chapter discusses 3R smoothing, where the expression “3R” means that the median smooth has been applied until no further changes were observed. It also discusses the concept of stem and leaf plot by providing an example of a small town mayor wanting to get some notion of the income distributions in his town. By using the data of random sample of yearly incomes of the example provided, the five figure summary and Tukey's box plot are discussed in the chapter. Controlled Vocabulary Terms box plot

Cited by 2