Graphics Programs Reference
In-Depth Information
9 Multivariate Statistics
9.1 Introduction
Multivariate analysis aims to understand and describe the relationship be-
tween an arbitrary number of variables. Earth scientists often deal with
multivariate data sets, such as microfossil assemblages, geochemical finger-
prints of volcanic ashes or clay mineral contents of sedimentary sequences.
If there are complex relationships between the different parameters, univari-
ate statistics ignores the information content of the data. There are number
of methods for investigating the scaling properties of multivariate data.
A multivariate data set consists of measurements of p variables on n ob-
jects. Such data sets are usually stored in n -by- p arrays:
The columns of the array represent the p variables, the rows represent the n
objects. The characteristics of the 2nd object in the suite of samples is de-
scribed by the vector in the second row of the data array:
As example assume the microprobe analysis on glass shards from volca-
nic ashes in a tephrochronology project. Then the variables represent the p
chemical elements, the objects are the n ash samples. The aim of the study is
to correlate ashes by means of their geochemical fi ngerprints.
The majority of multi-parameter methods simply try to overcome the
main diffi culty associated with multivariate data sets. This problem relates
to the data visualization. Whereas the character of an univariate or bivariate
Search WWH ::




Custom Search