Multivariate exploratory data analysis and graphics: A tutorial

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Abstract
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 expecteda 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.