exploratory actions - meaning and definition. What is exploratory actions
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What (who) is exploratory actions - definition

APPROACH OF ANALYZING DATA SETS IN STATISTICS
Exploratory analysis; Explorative data analysis; Exploratory statistics; Exploratory Data Analysis
  • Data science process flowchart

Marie Skłodowska-Curie Actions         
RESEARCH FUNDER
Initial Training Networks; User:Robinouze/Enter your new article name here; Initial Training Actions; Industry-Academia Partnerships and Pathways; User:Robinouze/Industry-Academia Partnerships and Pathways; Marie Curie Fellowship; Intra-European Fellowships; User:Robinouze/Intra-European Fellowships; International Incoming Fellowships; International Outgoing Fellowships; Career Integration Grants; Marie Curie Actions; Marie Sklodowska-Curie Actions
Marie Skłodowska-Curie Actions (MSCA) is a set of major research fellowships created by the European Union/European Commission to support research in the European Research Area (ERA). The Marie Skłodowska-Curie Actions are among Europe's most competitive and prestigious research and innovation fellowships.
exploratory         
WIKIMEDIA DISAMBIGUATION PAGE
Exploratory (disambiguation)
Exploratory actions are done in order to discover something or to learn the truth about something.
Exploratory surgery revealed her liver cancer...
ADJ
Exploratory         
WIKIMEDIA DISAMBIGUATION PAGE
Exploratory (disambiguation)
·adj Serving or intended to explore; searching; examining; explorative.

Wikipedia

Exploratory data analysis

In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling and thereby contrasts traditional hypothesis testing. Exploratory data analysis has been promoted by John Tukey since 1970 to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.