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Topic: Exploratory data analysis

	NationMaster - Encyclopedia: Ordination (statistics)
		In community ecology, ordination is a method of multivariate analysis complementary to data clustering, and used mainly in exploratory data analysis (rather than in hypothesis testing).
		Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics.
		In multivariate analysis, ordination is a method complementary to data clustering, and used mainly in exploratory data analysis (rather than in hypothesis testing).
	www.nationmaster.com /encyclopedia/Ordination-%28statistics%29 (515 words)

	Next-Generation Data Analysis Software Aids Faster Drug Development
		An important element of this exploratory approach is the software's flexibility to tailor the analysis to the data structure and to respond to patterns uncovered by successive analysis.
		Exploratory data analysis (EDA) is essential to population pharmacokinetic modeling, and new statistical software tools allow the data analyst to visualize and prototype data more efficiently.
		Exploratory data analysis provides powerful diagnostic tools for confirming assumptions or, when the assumptions are not met, for suggesting corrective actions.
	www.insightful.com /products/successstory.asp?SSID=18 (1624 words)

	Exploratory data analysis
		Exploratory data anaysis (EDA) is that part of statistical practice concerned with reviewing, communicating and using data where there is a low level of knowledge about its cause system.
		Tukey held that too much emphasis in statistics was placed on evaluating and testing given hypotheses (confirmatory data analysis) and that the balance was in need of redressing in favour of using data to suggest hypotheses to test.
		In particular, confusion of the two types of analysis and employing them on the same set of data can lead to bias owing to the effect of testing hypotheses suggested by the data.
	www.xasa.com /wiki/en/wikipedia/e/ex/exploratory_data_analysis.html (233 words)

	Libri.de - Natalia Andrienko, Gennady Andrienko: Exploratory Analysis of Spatial and Temporal Data
		Empfehlen Sie "Exploratory Analysis of Spatial and Temporal Data" weiter
		Natalia Andrienko, Gennady Andrienko: Exploratory Analysis of Spatial and Temporal Data
		Sie kennen Natalia Andrienko, Gennady Andrienko: Exploratory Analysis of Spatial and Temporal Data?
	www.libri.de /shop/action/productDetails/4019160/natalia_andrienko_gennady_andrienko_exploratory_analysis_of_spatial_and_temporal_data_3540259945.html (534 words)

	Data Desk Exploratory Data Analysis
		EDA was initially promoted by few statisticians, but in recent years it has grown in acceptance.
		Because EDA relies heavily on data display, makes few assumptions about the structure of the data and emphasizes identifying and describing patterns, it is useful to a wide range of professionals who can recognize important patterns easily, but may not wish to work with complex statistical techniques.
		EDA is the foundation of a growing trend that empowers people who have data and want to discover the patterns hiding within.
	www.kovcomp.co.uk /DataDesk/EDA.html (588 words)

	Data Mining Techniques
		One of the preliminary stage in predictive data mining, when the data set includes more variables than could be included (or would be efficient to include) in the actual model building phase (or even in initial exploratory operations), is to select predictors from a large list of candidates.
		Data reduction is another possible objective for data mining (e.g., to aggregate or amalgamate the information in very large data sets into useful and manageable chunks).
		The exploration of data can only serve as the first stage of data analysis and its results can be treated as tentative at best as long as they are not confirmed, e.g., crossvalidated, using a different data set (or and independent subset).
	www.statsoft.com /textbook/stdatmin.html (4347 words)

	1.1.1. What is EDA?
		EDA is not identical to statistical graphics although the two terms are used almost interchangeably.
		EDA encompasses a larger venue; EDA is an approach to data analysis that postpones the usual assumptions about what kind of model the data follow with the more direct approach of allowing the data itself to reveal its underlying structure and model.
		EDA is not a mere collection of techniques; EDA is a philosophy as to how we dissect a data set; what we look for; how we look; and how we interpret.
	www.itl.nist.gov /div898/handbook/eda/section1/eda11.htm (392 words)

	Data Mining with STATISTICA
		It offers not only a plethora of specialized exploratory data analysis techniques (uniquely integrated with facilities to test if the results of your explorations are conclusive) but also the most powerful graphical data mining procedures available in the industry, integrated with ALL your views of data and numerical results.
		Exploratory Data Analysis (EDA) is that Data Mining is more oriented towards applications than the basic nature of the underlying phenomena.
		In a typical exploratory data analysis process, many variables are taken into account and compared, using a variety of techniques in the search for systematic patterns.
	www.mpassociates.gr /software/distrib/science/statsoft/datamine.html (2355 words)

	Exploratory Data Analysis (Site not responding. Last check: 2007-10-14)
		However, in addition to advocating the graphical techniques of visual data analysis, he proposed the methodology of data exploration, a methodology in which a model of the phenomena might be inferred instead of pre-imposed.
		The exploratory approach is very appropriate for data analysis because it allows you to explore your data with an open mind.
		Please note that it is common, though incorrect, to think of exploratory data analysis as being synonymous with visual data analysis.
	www.datamology.com /eda.shtml (400 words)

	1.5 Exploratory Data Analysis (EDA)
		Exploratory data analysis (EDA) provides a simple way to obtain a big picture look at the data, and a quick way to check data for mistakes to prevent contamination of subsequent analyses.
		Exploratory data analysis can be thought of as preliminary to more in depth statistical data analysis.
		You can see then that the semi-interquartile range value for ordinal data can be thought of as being conceptually similar to the standard deviation for quantitative data.
	www.uth.tmc.edu /uth_orgs/educ_dev/oser/L1_5.HTM (935 words)

	Data Mining Techniques
		Data Mining is often considered to be "a blend of statistics, AI [artificial intelligence], and data base research" (Pregibon, 1997, p.
		A large selection of powerful exploratory data analytic techniques is also offered by graphical data visualization methods that can identify relations, trends, and biases "hidden" in unstructured data sets.
		Perhaps the most common and historically first widely used technique explicitly identified as graphical exploratory data analysis is brushing, an interactive method allowing one to select on-screen specific data points or subsets of data and identify their (e.g., common) characteristics, or to examine their effects on relations between relevant variables.
	www3.baylor.edu /~Jack_Tubbs/StatSoft/stdatmin.html (1989 words)

	IMPLEMENTING EXPLORATORY SPATIAL DATA ANALYSIS METHODS FOR MULTIVARIATE HEALTH STATISTICS
		The use of geographically referenced mortality data to detect disease "hot spots" can be traced, at least, to Dr. John Snow’s 1854 map of cholera deaths in London, which allowed him to hypothesize that a particular water pump was the source of the epidemic.
		GVis, then, combines the work in ViSC and EDA with the principles of cartography to produce multivariate, and spatio-temporal representations that allow the analyst to address problems that were difficult, or impossible to deal with using static, univariate, or non-visual representations.
		Therefore the data range for all possible death rates in all possible years must be used to determine the axis range.
	www.geovista.psu.edu /publications/GISLIS97/index.html (4629 words)

	Sonification for Exploratory Data Analysis - Neuroinformatics Group - Bielefeld University
		Data Sonograms are spatial sonification models, where excitation waves expand spherically in model space and excite the data points that are represented by spring mass models
		Data Solids are spring-mass models of data objects.
		We are interested in generic data exploration techniques, that on the one hand can be used for a large class of data sets yet remain flexible for later adjustment.
	www.techfak.uni-bielefeld.de /ags/ni/projects/son (744 words)

	Actuarial Review - August 2006 - Ensuring High Caliber Data
		In data mining circles this book is the reference of choice on data quality and its authors are invited to speak on the topic at many conferences.
		Exploratory data mining is an application of exploratory data analysis to large databases that can be used to understand the structure of a database and to detect outliers (data glitches are often found by examining outliers).
		Data depth provides a measure of how far a record is from the center of the data or from typical data values.
	www.casact.org /newsletter/index.cfm?fa=viewart&id=5263 (857 words)

	CRC Press Online (Site not responding. Last check: 2007-10-14)
		Exploratory data analysis (EDA) was conceived at a time when computers were not widely used, and thus computational ability was rather limited.
		There are many resources for those interested in the theory of EDA, but this is the first book to use MATLAB to illustrate the computational aspects of this discipline.
		Exploratory Data Analysis with MATLAB presents the methods of EDA from a computational perspective.
	www.crcpress.com /e_products/downloads/download.asp?id=&parent_id=1021&sku=C3669&cat_no=C3669&isbn=&pc=&page_detail=true (221 words)

	Exploratory Data Mining and Data Cleaning - Wiley-Interscience Tamraparni Dasu & Theodore Johnson (Site not responding. Last check: 2007-10-14)
		Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.
		Data Mining Techniques: Data Preparation (in Data Mining) Data preparation and cleaning is an often neglected but...
		Other types of data mining projects may be more exploratory in nature (e.g., to identify cluster or...
	www.skattabrain.com /css-books-plain/0471268518.html (381 words)

	Hypothesis-Driven and Exploratory Data Analysis
		It was the job of the ecologist to use his or her knowledge and intuition to collect and interpret data; pure objectivity could potentially interfere with the ability to distinguish important gradients.
		The purpose of exploratory analysis is to find pattern in nature, which is an inherently subjective enterprise.
		Indeed, an exploratory analysis can be aided if the investigator subjectively places study plots in locations he or she considers to be important or interesting.
	www.okstate.edu /artsci/botany/ordinate/motivate.htm (995 words)

	Revista Árvore - Use of exploratory data analysis and robust regression to evaluate the growth of commercial species ... (Site not responding. Last check: 2007-10-14)
		The objective of the research was to use exploratory data analysis and robust regression for modeling diameter and basal area growths.
		The statistical analyses were preceded by exploratory data analysis (EDA), where the box plot was used for outliers detection, and the stem-and-leaf graph to filter the extreme observations.
		It was also shown that the use of exploratory data analysis and robust regression made possible the analysis and determination of periodic increments in diameter and basal area in a consistent way.
	www.scielo.br /scielo.php?script=sci_arttext&pid=S0100-67622002000400001 (3756 words)

	Exploratory Data Analysis (Site not responding. Last check: 2007-10-14)
		In this example, the single data "outlier" (arrow) would heavily influence the result of any statistical analysis.
		It is important to investigate the cause of this isolated datapoint (e.g.
		EDA shows you the patterns which are hidden when the data is in numerical form.
	www.microbiologybytes.com /maths/DH2.html (552 words)

	Interactive Maps for Exploratory Data Analysis (Site not responding. Last check: 2007-10-14)
		The importance of exploratory data analysis (EDA) as a prerequisite to application of computational methods, such as traditional statistical analysis, is currently widely recognised.
		For visualization of such data, maps are traditionally used, since they are isomorphic to space and thus capable of representing and conveying to human’s eye significant spatial relationships.
		The general topic of the proposed tutorial is visualisation of spatial data as a tool for exploratory data analysis, problem solving, and decision-making.
	www.commongis.com /tutorial/tutorial-PKDD-2003.html (1154 words)

	Psyc344 and Psyc506: Exploratory and Graphical Data Analysis (Site not responding. Last check: 2007-10-14)
		Exploratory data analysis completes this research cycle by helping to form and change new theories.
		After the planned hypothesis testing for an experiment is finished, exploratory data analysis can look for patterns in these data that may have been missed by the original hypothesis tests.
		A second use of exploratory data analysis is in diagnostics for hypothesis tests.
	www.nd.edu /%7Esboker/Psyc344506-2004.html (1105 words)

	Mathematics and Social Sciences
		From mathematics, MSS is for students interested in statistics, data analysis, mathematics, or computer sciences directed toward application in social science.
		Emphasis is placed on the understanding, use, and both oral and written interpretation of exploratory data analysis within the rules of scientific method.
		Prior knowledge of elementary data analysis or elementary statistics is assumed.
	www.dartmouth.edu /%7Ereg/courses/desc/m_ss.html (764 words)

	Exploratory data analysis (Site not responding. Last check: 2007-10-14)
		ben_allshouse@unc.edu), and provide either an exploratory analysis of the available data, or, if you are not able to obtain any data, write a proposal outlining what would be the steps to obtain such data at the smallest cost (i.e.
		Data for ENVR 468 link on the class website.
		Write a report describing your exploratory data analysis, providing all the relevant figures, and discussing any interesting findings.
	www.unc.edu /~mserre/teaching/fall2006/envr468/homework/hwk4/ENVR468hwk4.htm (242 words)

	ETSU Online Database (Site not responding. Last check: 2007-10-14)
		This course builds on the foundations of research and statistics and introduces multivariate statistical techniques commonly used in educational research.
		It develops skills in parametric and nonparametric analysis, survey design and scale construction, database development and management, and the use of statistical analysis packages.
		demonstrate the ability to conduct complex data analyses and interpretations for policy decision-making.
	www.etsu.edu /dbonline/cis/details.asp?Action=CourseDetails&RCNOID=1123 (273 words)

	Exploratory Analysis — Avian Knowledge Network (Site not responding. Last check: 2007-10-14)
		Exploratory analyses are an integral part of the process of scientific discovery, helping to generate testable hypotheses for further investigation and providing support for data-based decision making.
		However, for the Avian Knowledge Network’s (AKN) large assemblage of natural history data, with millions of data records and hundreds of potential predictors of birds' distribution and abundance, efficient and relatively automated tools need to be used in data exploration.
		Within the last decade, data mining and machine learning techniques have emerged as some of the most successful tools for modeling complex, multi-dimensional data.
	www.avianknowledge.net /exploratory_analysis (285 words)

	Tobler.SoftwareArchitecture() : [General] Exploratory Data Analysis (EDA)
		I have long been fascinated by Exploratory Data Analysis (EDA), a very creative new statistical methodology that differs substantially from what most people know as statistics.
		Sometimes, you end up with a bunch of data and have absolutely no idea what might be "in there." Tukey's methods included some very interesting graphical techniques, such as "stem and leaf diagrams" and "box plots," that stand as excellent early modern data visualization examples.
		The immediate motive for this post is that I just discovered two nice introductory sites about EDA that I had not previously seen: Exploratory Data Analysis and Data Visualization, by the unusual Dr.
	weblogs.asp.net /jtobler/archive/2004/08/22/218747.aspx (533 words)

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