I'r - Memorial University of Newfoundland

Selecting lIt1J'iahlu: It was demonstrated that to ovm;ome the probkm of
dimensionality in mult ivariate statistics the ratio of Dumberof cases coDurnbet' of
variables should not exceed three (Howarth andSinding.Larscn. 1983). Thus. for a data
set consisting of23 cases no more than 7 variables can be util lzcd at a time.. Therefore
the first proble m was selection of7 variables for each data sec which would represent the
wh ole PAM assemblage in further analyses. Altbough different approaches to variable
selection were used,a precaution was alw ays taken to keep compo wtds with different
mo lecular weigh ts more or less equally represented.
One ofthe approaches to variable selection is ana lysis o f thc correlation matrix for
the data set andexcluding one oftwo highly correlatedvariables (e.g.. Vajnovslcy and
Malinin. 1992). This approach was accepted fOI" the data on molecular composition. The
significance of correlations within the mo lecular data set is represented by Figure 3.2.2
(5C:e also Appendix AS). This figure shows that most cfthe variables are highly
correlated. The seven least correlated variables (Ae, Fl, Fa, BbF, Per, DBA, BP) were
chosen for further statistical analyses .
A similar analysis of the correlation matrix for isotopic compositioo. data (Appendix.
A9 ) showed much lower significance levels (Fig. 3-2.3). This implied that in this case
mo st variables could not be predicted from each other. However. since determination of
SIlC in many cases was impeded by low signal to noise ratio or low concentrations, the
data set contains numerous cases with missing values (Appendix Al). Therefore, the
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