Abstract
Each object of multivariate data distributed in a multidimensional space is characterised by a set of measurements corresponding to each variable. Obviously, as we have already seen (Chap. 9), if we are dealing with bivariate data corresponding to two variables, a plot on a piece of paper or computer screen can show their behaviour. Their graphical representation becomes difficult and, in fact, impossible when we want to visualise four or more dimensions. Therefore, we must learn alternate techniques for the handling of multivariate data. We describe the dimension-reducing multivariate technique of linear discriminant analysis (LDA) and illustrate it from agricultural chemistry data. One surprising result was that the isometric log-ratio transformation did not provide any improvement with respect to the use of concentration data. The chapter ends with the description of multiple linear regression exemplified from UV absorbance data.
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Verma, S.P. (2020). Multivariate Data. In: Road from Geochemistry to Geochemometrics. Springer, Singapore. https://doi.org/10.1007/978-981-13-9278-8_10
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DOI: https://doi.org/10.1007/978-981-13-9278-8_10
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