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Multivariate exploratory data analysis and graphics: A tutorial
Author(s) -
Weihs C.
Publication year - 1993
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1180070502
Subject(s) - univariate , computer science , multivariate statistics , exploratory data analysis , dimensionality reduction , interpretability , data mining , toolbox , bivariate analysis , data visualization , resampling , multivariate analysis , data extraction , graphics , artificial intelligence , machine learning , visualization , medline , political science , law , programming language , computer graphics (images)
Exploratory data analysis (EDA) is a toolbox of data manipulation methods for looking at data to see what they seem to say, i.e. one tries to let the data speak for themselves. In this way there is hope that the data will lead to indications about ‘models’ of relationships not expected a priori . In this respect EDA is a pre‐step to confirmatory data analysis which delivers measures of how adequate a model is. In this tutorial the focus is on multivariate exploratory data analysis for quantitative data using linear methods for dimension reduction and prediction. Purely graphical multivariate tools such as 3D rotation and scatterplot matrices are discussed after having introduced the univariate and bivariate tools on which they are based. The main tasks of multivariate exploratory data analysis are identified as ‘search for structure’ by dimension reduction and ‘model selection’ by comparing predictive power. Resampling is used to support validity, and variables selection to improve interpretability.