For example, in all but the last of these columns we were concerned with the analysis of simple morphological data. However, it's a big world out there and we'd often like to know how one type of data relates to another type of data. Using our trilobite dataset as an example, if we are asking questions about this particular assemblage of 20 trilobite genera the results we have obtained to date are perfectly valid. An implicit assumption that runs across all the methods we've discussed so far (bivariate regression, multivariate regression, PCA, Factor Analysis, PCOORD, and correspondence analysis) is that the objects included in the dataset represent independent and randomly selected samples drawn from a population of interest. In the last four columns we've looked at problems associated with characterizing and identifying patterns in single datasets. Note: This article has not been updated to the new website style, using html tables rather than embeded images, there may be presentation issues.
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