R is a statistical
environment, that allows quick-and-dirty univariate
statistical analyses and plots, as well as complex multivariate
analyses, bayesian statistics and computational solutions. It has
become a de facto standard of statistics.
This course covers the R instructions to produce the most used univariate statistical techniques, as well as principal component analyses. It is good that attendants have followed a basic statistics course, though the several methods and concepts will be reviewed and illustrated during the lessons.
More information per e-mail or here
Did you notice that your data sets have lots of variables? and that they are typically never negative? Or that they are mostly given in relative units, like ppm, g/l, wt% or mol%? They are compositional data. Compositional data, just due to these characteristics, cannot be analysed naively with standard techniques. This course will present in detail the basic multivariate statistics, how to apply them and interpret their results, with a special focus on compositional data sets.
Geostatistics is the name given to a set of statistical techniques to analize spatially-referenced data (i.e. we know where the samples were collected) and interpolate them, assessing the quality of this interpolation.
More information per e-mail or here (attention, only available when I'm in the office)
This course introduces the most useful statistical methods to analyse data sets with multiple variables. The basics of each method are presented, detailing its usefulness, assumptions, requirements, weaknesses and typical results. The free statistical software R is used to do the analysis.
Goal: Introduction to the theory and practice of the statistical treatment of compositions (multidimensional observations of positive components, where each part tells us the importance of a part in a whole).
Lecture Notes on Compositional Data Analysis