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Multivariate Data,iously, as we have already seen (Chap. .), 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 diLineage 发表于 2025-3-25 11:48:19
Multidimensional Techniques for Compositional Data Analysis,es, except the widely known classic work of Aitchison (The statistical analysis of compositional data. Chapman and Hall, London, UK, 1986) and a little-known recent book in Spanish by Verma (Análisis estadístico de datos composicionales. Universidad Nacional Autónoma de México, CDMX, 2016). The serientitle 发表于 2025-3-25 16:46:21
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examples/exercises.Helps students acquaint themselves with .This book highlights major problems in the statistical analysis of compositions that have been known for over a century, as well as the corresponding solutions that have been put forward by specialists over the past 30 years. The basic assMUTED 发表于 2025-3-26 12:11:34
Trace Element Geochemistry,esentation and usefulness of combined ratio parameters for drastically reducing the number of bivariate or ternary diagrams and providing a better innovative geochemometric alternative for the handling of geochemical data. It is interesting to see how all these quantititative parameters are useful for solving geological problems.六边形 发表于 2025-3-26 14:53:29
Significance Tests,etail. The inherent assumption of normality requires that discordant outlier-free data be used for FICI. The chapter ends with a brief explanation of the critical sample sizes for the “fit-for-purpose” concept, most useful for planning an experiment.agnostic 发表于 2025-3-26 17:05:31
Multivariate Data,ue 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.