Exploratory Data Analysis (EDA): Difference between revisions
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{{Definition|'''Exploratory data analysis (EDA)''' was introduced by [[John Tukey]] as an approach to analyze data when there is only a low level of knowledge about its cause system as well as ''contextual'' information. EDA aims at letting the data itself influence the process of suggesting hypotheses instead of only using it to evaluate given ''(a priori)'' hypotheses.}} | {{Definition|'''Exploratory data analysis (EDA)''' was introduced by [[John Tukey]] as an approach to analyze data when there is only a low level of knowledge about its cause system as well as ''contextual'' information. EDA aims at letting the data itself influence the process of suggesting hypotheses instead of only using it to evaluate given ''(a priori)'' hypotheses.}} | ||
Revision as of 20:15, 14 April 2005
Exploratory data analysis (EDA) was introduced by John Tukey as an approach to analyze data when there is only a low level of knowledge about its cause system as well as contextual information. EDA aims at letting the data itself influence the process of suggesting hypotheses instead of only using it to evaluate given (a priori) hypotheses.
Furthermore, EDA can be used to support the selection of appropriate statistical tools as well as to provide a basis for statistical inference and further data collection.
Essential to EDA are graphical tools like box plots, stem–and–leaf plots or scatter plots.