exploratory data analysis


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Exploratory Data Analysis

(EDA)

[J.W.Tukey, "Exploratory Data Analysis", 1977, Addisson Wesley].

exploratory data analysis

a form of statistical analysis which begins by exploring data rather than testing clearly formulated prior hypotheses. Exploratory data analysis does as it says: it explores the pattern of the data set under analysis, considering its range, level, outliers, batching it before graphing and transforming it. The MINITAB computer package, for example, contains these techniques in its sub-pro grammes. In either qualitative or quantitative forms, the purpose of exploratory data analysis is to follow parallel procedures in the interrogation of statistics, i.e. generating hypotheses through exploring the data before turning to confirmatory statistics to test those hypotheses.
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Ho Yu, "Exploratory data analysis in the context of data mining and resampling.," International Journal of Psychological Research, vol.
Exploratory data analysis is detective work--numerical detective work--or counting detective work--or graphical detective work....
The following section extends these methods to develop robust GWR models that yield parameters that are especially useful in exploratory data analysis. The final section contains a brief summary of the methods developed and comments with respect to their application.
A 2012 survey by the eHealth Initiative (EHI) and the College for Health Information Management Executives (CHIME) revealed that while more than half of respondents reported using ad-hoc queries, data mining and data warehousing, less than half reported use of exploratory data analysis and online analytical processing.
Its power to inform exploratory data analysis has gained it a place in statistics curricula at various levels in many countries.
Keywords: Atmospheric phenomenon, Ozone layer Depletion, stratospheric region, exploratory data analysis, coefficient of variations.
They cover experimental design and optimization considerations; exploratory data analysis, prediction, and classification; quantitative structure relationships; and transformation techniques.
Exploratory data analysis. Reading (MA): Addison-Wesley; 1977.
For the purposes of exploratory data analysis, we considered species seeded or present at more than 5 sites to be "frequent" enough to assess patterns.
Software is important for successful implementation of graphical techniques within exploratory data analysis. Although widely employed electronic commercial spreadsheet software like Microsoft's Excel is capable and flexible for the traditional application of graphs, it is ill-suited for data discoveries of a statistical nature.
State and MMSA data can be displayed independently or in combination to facilitate exploratory data analysis of within-state variations and identification of regional patterns.
The importance of exploratory data analysis as a means of gaining

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