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The same variables for fluting were then calculated. Note: creating a digital model of an artefact using a desktop 3D scanner does not in any way violate a copyright. It is not an infringement of copyright in any way.
The data were then entered into software in the same way as described above for the raw data and then the analysis of the predictor variables was repeated. This forms the dependent variable (e.g. dependent variable = plan symmetry) which can be entered into a statistica program to test the differences in the principal knapping variables between the groups.
Subsequent analysis was then conducted. For example, to test the statistical variance within each group, a Kolmogorov-Smirnov test was performed and the results were then visually displayed as probability/ceiling curves for each variable. As explained above, where there are clear segmented clusters in the probability/ceiling curve, this would suggest that there are significant differences in the raw scores for the variables between the different groups. This would mean that learning was taking place in some of the other groups as well.
The dependent variable from each group described above was then entered into a multiple regression formula to see if any of the variables described above were significant predictors of the dependent variable.