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For most variates the values analysed were the sum of the values determined during the period of analysis.
Of the thirty-nine main effects tested (13 variates, three treatments) only four showed significant effects at the 5 % probability level.
The dimension of each item measured is called the variate and may be any physical quantity.
These models have variates that are serially dependent but nevertheless unforecastable.
All variates were first screened for outliers and for normality.
The morphological variation was investigated using both canonical variates analysis and a novel non-parametric classification tree method.
In effect, the use of brushing makes it possible to show an increased number of variates.
The first two canonical variates, showing 84.5% of the separation, are plotted.
However, this function involves a multivariate integral whose variates are correlated.
The variates are called 'iid' if they are identically as well as independently distributed.
The scores of the first two canonical variates separated the tobaccofeeding clones from those originating from other secondary host-plants.
Nevertheless, some meaningful conclusions can be drawn on the possible value of facial variates in the study of human interand intra-group variability.
The first involves applying the test to two realizations of white-noise variates generated from a pseudorandom number generator.
All other variates and statistics have physical dimensions which must be stated clearly.
Depending on the dimension of the emission variates, on the number of components, and on the available computational resources, one can vary the accuracy of the adjustment.
Also, aberrant values can result from a bad choice of variate.
However, this clone was not classified as the tobacco form based on canonical variate analysis.
Canonical variate analysis, hierarchical cluster analysis and a non-parametric classification tree method both revealed morphological differences associated with the host-plant on which they were collected.
Figure 4 is a plot of the first two canonical variates, which together account for 49% of the total variation of data.