The contingency table: a powerful tool of multivariate statistics
Published:January 01, 2006
As in other areas of geophysics or meteorology, the observations and data collected at volcanoes are the result of experiments in which we cannot control the variables we wish to study. Thus, statistical analysis is an extremely important step in the data processing. Variations in the experimental parameters must be controlled through the choice of samples and through the hypotheses chosen for testing. The evaluation of the samples is possible only through the application of the proper statistical methods, especially multivariate statistics.
Volcanic activity is the manifestation of complex dynamic processes and interactions within the volcano. It dependson the movement of fluids as well as on the thermodynamics of the magma and gases within a branched network of conduits and cavities. The dynamic processes generate various geophysical signals, as well as visible phenomena at the volcano’s surface. State-of-the-art techniques for monitoring at volcanoes now include continuous and concurrent recording of a variety of both quantitative and qualitative observations using a multi-parameter station in the near-field of thecrater. Such a station has been installed at Galeras volcano in Colombia, and has been operating for several years (Seidl et al. 2003). Presumably, the signals and phenomena observed at the surface of a volcano have acommon source in terms of the strong interactions between various internal processes. Thus, data from different measurements should show a significant correlation. Contingency tables are a powerful statistical method for investigating such multi-dimensional correlations between quantitative and qualitative data. The pattern of signals and phenomena, aswell
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Statistics in Volcanology
Statistics in Volcanology is a comprehensive guide to modern statistical methods applied in volcanology written by today's leading authorities. The volume aims to show how the statistical analysis of complex volcanological data sets, including time series, and numerical models of volcanic processes can improve our ability to forecast volcanic eruptions. Specific topics include the use of expert elicitation and Bayesian methods in eruption forecasting, statistical models of temporal and spatial patterns of volcanic activity, analysis of time series in volcano seismology, probabilistic hazard assessment, and assessment of numerical models using robust statistical methods. Also provided are comprehensive overviews of volcanic phenomena, and a full glossary of both volcanological and statistical terms.
Statistics in Volcanology is essential reading for advanced undergraduates, graduate students, and research scientists interested in this multidisciplinary field.