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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." [3] Exploratory data ...
Tukey emphasized the importance of having a more flexible attitude towards data analysis and of exploring data carefully to see what structures and information might be contained therein. He called this "exploratory data analysis" (EDA). In many ways, EDA was a precursor to data science. Tukey also realized the importance of computer science to ...
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Pages in category "Exploratory data analysis" The following 13 pages are in this category, out of 13 total. This list may not reflect recent changes. ...
Exploratory research is "the preliminary research to clarify the exact nature of the problem to be solved." It is used to ensure additional research is taken into consideration during an experiment as well as determining research priorities, collecting data and honing in on certain subjects which may be difficult to take note of without exploratory research.
The LTGA [15] differs from most EDA in the sense it does not explicitly model a probability distribution but only a linkage model, called linkage-tree. A linkage T {\displaystyle T} is a set of linkage sets with no probability distribution associated, therefore, there is no way to sample new solutions directly from T {\displaystyle T} .
Exploratory Factor Analysis Model. In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables. [1]
Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. [1]