Abstract
Mineral resource estimation requires extensive use of statistics. In our context, statistics are mathematical methods for collecting, organizing, and interpreting data, as well as drawing conclusions and making reasonable decisions based on such analysis. This chapter presents essential concepts and tools required throughout the book.
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Abramovitz M, Stegun I (1964) Handbook of mathematical functions. Dover Publications, New York, p 1046
Anderson T (1958) An introduction to multivariate statistical analysis. Wiley, New York
Borradaile GJ (2003) Statistics of earth science data. Springer, Heidelberg
David M (1977) Geostatistical ore reserve estimation. Elsevier, Amsterdam
Davis JC (1986) Statistics and data analysis in geology, 2nd edn. Wiley, New York, p 646
de Moivre A (1738) The doctrine of chances: or, a method for calculating the probabilities of events in play, 2nd edn. printed by H. Woodfall, London
Deutsch CV (1989) DECLUS: A FORTRAN 77 program for determining optimum spatial declustering weights. Comput Geosci 15(3):325–332
Deutsch CV (2002) Geostatistical reservoir modeling. Oxford University Press, New York, p 376
Deustch CV, Journel AG (1997) GSLIB: geostatistical software library and user’s guide, 2nd edn. Oxford University Press, New York, p 369
Dillon W, Goldstein M (1984) Multivariate analysis: methods and applications. Wiley, New York, p 587
Gauss CF (1809) Theoria Motus Corporum Coelestium in sectionibus conicis solem ambientium. English translation by C. H. Davis, reprinted 1963, Dover, New York
Goovaerts P (1997) Geostatistics for natural resources evaluation. Oxford University Press, New York, p 483
Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York, p 561
Journel AG (1983) Non-parametric estimation of spatial distributions. Math Geol 15(3):445–468
Journel AG, Huijbregts ChJ (1978) Mining geostatistics. Academic Press, New York
Journel AG, Rossi ME (1989) When do we need a trend model? Math Geol 22(8):715–739
Koch G, Link R (1986) Statistical analysis of geological data, 2nd edn. Wiley, New York
Lapin LL (1983) Probability and statistics for modern engineering. PWS Publishers, Boston, p 624
Laplace PS (1812) Théorie analytique des probabilités. Printed in 1814 by Mme. Ve. Courier, Paris
Legendre AM (1806) Nouvelles Methodes pour la Determination des Orbites des Cometes. F. Didot, Paris
Parker HM (1991) Statistical treatment of outlier data in epithermal gold deposit reserve estimation. Math Geol 23:125–199
Ripley BD (1987) Spatial statistics, 2nd edn. Wiley, New York
Rohatgi VK, Ehsanes Saleh AK Md (2000) An introduction to probability and statistics. Wiley, New York
Sichel HS (1952) New methods in the statistical evaluation of mine sampling data. Trans Inst Min Metall Lond 61:261
Verly G (1984) Estimation of spatial point and block distributions: the multigaussian model. PhD Dissertation, Department of Applied Earth Sciences, Stanford University
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Rossi, M., Deutsch, C. (2014). Statistical Tools and Concepts. In: Mineral Resource Estimation. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5717-5_2
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DOI: https://doi.org/10.1007/978-1-4020-5717-5_2
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