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Compared to the past, ever larger amounts of data are being collected
in astronomy, and the rate will continue to accelerate in the next
decades. It is therefore necessary to work on bigger samples if full
advantage is to be taken of all accessible information. It is also
necessary to derive as much information as possible from the diversity
of the data, rather than restricting attention to subsets of it.
One way to work effectively on large samples is to apply, and if
necessary to develop, suitable statistical methods. Multivariate data
analysis methods are not intended to replace physical analysis: these
should be seen as complementary, and statistical methods can
effectively be used to run a rough preliminary investigation, to sort
out ideas, to put a new (``objective'' or ``independent'') light on a
problem, or to point out aspects which would not come out in a
classical approach. Physical analysis is necessary subsequently to
refine and interpret the results.

The multivariate methods implemented all operate on MIDAS tables.
Such tables cross objects (e.g. ultraviolet spectra, spiral galaxies,
or stellar chemical abundances) with variables or parameters.

Among widely-used multivariate methods :

- 1.
- Principal Components Analysis
- 2.
- Hierarchical Cluster Analysis
- 3.
- Non-Hierarchical Clustering, or Partitioning
- 4.
- Minimal Spanning Tree
- 5.
- Discriminant Analysis
- 6.
- Correspondence Analysis

We will look briefly at each of these in turn. Since comprehensive
ancillary documentation is available (see references), we will not
dwell on background material here. The wide-ranging applications for
which multivariate statistical methods have been employed in astronomy
also cannot be catalogued here (again, see the references given). In
the following sections we will concentrate on various practical
aspects of the methods. Note that the MVA *context* is required
to activate the relevant commands (by using the command `SET/CONTEXT MVA` in a MIDAS session).

** Next:** Principal Components Analysis
** Up:** Multivariate Analysis Methods
** Previous:** Multivariate Analysis Methods
*Petra Nass*

*1999-06-15*