Shape analysis of paleosecular variation data

Abstract

Shape analysis of paleosecular variation (PSV) data, if it is properly used, can determine the statistical character of a PSV data set in many cases. Existing statistical techniques, such as the χ2 goodness of fit test, the ‘dipole moment,’ and eigenvalue methods, are reviewed with this point in mind. Two new parameters, K0 and Km, are introduced that can be used to derive a better estimate of angular standard deviation for a PSV data set, particularly if the data are most densely distributed near their mean. We have analyzed the shape of the distributions of 12 PSV data sets of the Brunhes and Matuyama epochs by means of these various statistical techniques. The results of our studies indicate that the minimum size of a PSV data set should be about 30 and that the shape analysis already mentioned should be used as one PSV data selection criterion.