Abstract
The main characteristics of multivariate data are discussed, and exploratory tools are provided to extract information from them. Different preprocessing strategies of normalization, smoothing and compression are described and compared for the cases of discrete and continuous variables. Finally, principal component analysis (PCA) is studied as an especially useful strategy for the unsupervised discrimination of samples with examples from the electroanalytical literature.
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Díaz-Cruz, J.M., Esteban, M., Ariño, C. (2019). Exploratory Data Analysis. In: Chemometrics in Electroanalysis. Monographs in Electrochemistry. Springer, Cham. https://doi.org/10.1007/978-3-030-21384-8_3
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DOI: https://doi.org/10.1007/978-3-030-21384-8_3
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