Exploratory data analysis: Difference between revisions

Revision as of 06:00, 21 March 2024

Revision as of 21:13, 7 May 2024

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{{short description|Approach of analyzing data sets in statistics}}

{{Data Visualization}}

[[File:Optimizing edge intelligence.png|thumb|Exploratory Data Analysis: Unveiling Insights into Edge Intelligence Enhancement. In this comprehensive exploration, the graph traces the trajectories of two curves - one representing the quantitative assessment model for edge intelligence enhancement, and the other showcasing actual test results. Both embark from the origin (0,1) and converge meaningfully at (80,70), indicating a shared comprehensive proportion during this pivotal phase. Intriguingly, as the data unfolds beyond this point, a discernible divergence emerges. The Edge Intelligence Enhancement Model consistently surpasses actual test results, revealing a compelling reserve in comprehensive proportions. This nuanced visual narrative provides valuable insights into the intricate dynamics between modeled predictions and empirical outcomes, underscoring the significance of exploratory data analysis in unraveling the complexities of enhanced edge intelligence.]]

In [[statistics]], '''exploratory data analysis''' (EDA) is an approach of [[data analysis|analyzing]] [[data set]]s 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 [[Data analysis#Initial data analysis|initial data analysis (IDA)]],<ref>{{cite book |last=Chatfield |first=C. |year=1995 |title=Problem Solving: A Statistician's Guide |publisher=Chapman and Hall |isbn=978-0412606304 |edition=2nd }}</ref><ref>{{cite journal |doi=10.1371/journal.pcbi.1009819|title=Ten simple rules for initial data analysis|year=2022|last1=Baillie|first1=Mark|last2=Le Cessie|first2=Saskia|last3=Schmidt|first3=Carsten Oliver|last4=Lusa|first4=Lara|last5=Huebner|first5=Marianne|author6=Topic Group "Initial Data Analysis" of the STRATOS Initiative|journal=PLOS Computational Biology|volume=18|issue=2|pages=e1009819|pmid=35202399|pmc=8870512|bibcode=2022PLSCB..18E9819B |doi-access=free }}</ref> 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.