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dc.contributor.authorLewis, Stephen J.*
dc.date.accessioned2009-04-14T13:10:52Z
dc.date.available2009-04-14T13:10:52Z
dc.date.issued1997-12-01
dc.identifier.citationIn K. Boyle & S. Anderson (Eds.), Computing and statistics in osteoarchaeology (pp. 31-34). Oxford: Oxbow Books, 1997.
dc.identifier.isbn1900188465
dc.identifier.urihttp://hdl.handle.net/10034/64836
dc.descriptionThis is the author's PDF version of an book chapter published in Computing and statistics in osteoarchaeology ©1997. The paper was originally delivered at the second meeting of the Osteoarchaeological Research Group at the Institute of Archaeology, University College, London on 8 April 1995.
dc.description.abstractThe numerical output of multivariate statistical analyses may extend to a greater number of dimensions than can be comprehended and so may appear abstract and divorced from the original data. A need arises, therefore, for the provision of a more intuitive understanding of the results of such techniques - perhaps of a graphical nature. A simple method is to plot, what have come to be known as, Andrews' curves. A tabular procedure, using a standard computer spreadsheet, is described whereby the coefficients produced by various multivariate statistical techniques can be substituted into a simple equation to produce a smooth, wave-like curve characterising the source data. Importantly, this technique also provides a means whereby groups of curves may be compared visually to identify clusters and curves of similar or dissimilar overall shape. Similarly, "outliers" may also be spotted.
dc.language.isoenen
dc.publisherOxbow Books (for The Osteoarchaeological Research Group)
dc.relation.urlhttp://www.oxbowbooks.com/home.cfmen
dc.subjectvisualizing multivariate analysisen
dc.titleVisualizing multivariate analysis - An intuitive approach to high dimensional statistical extractionsen
dc.typeBook chapteren
dc.contributor.departmentChester College of Higher Education
html.description.abstractThe numerical output of multivariate statistical analyses may extend to a greater number of dimensions than can be comprehended and so may appear abstract and divorced from the original data. A need arises, therefore, for the provision of a more intuitive understanding of the results of such techniques - perhaps of a graphical nature. A simple method is to plot, what have come to be known as, Andrews' curves. A tabular procedure, using a standard computer spreadsheet, is described whereby the coefficients produced by various multivariate statistical techniques can be substituted into a simple equation to produce a smooth, wave-like curve characterising the source data. Importantly, this technique also provides a means whereby groups of curves may be compared visually to identify clusters and curves of similar or dissimilar overall shape. Similarly, "outliers" may also be spotted.


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