Analysis and Interpretation Data of Exploration

A review on classic statistics including different conventional exploratory statistical distributions, estimation of statistical significance levels, data conversion to a normal distribution, central limit theorems, normal distribution, logratio transformation techniques, Box-Cox Transformations, Separation methods bimodal distributions, statistical inferences in exploration activities including levels of significance, one-way and two-way analysis of variance and its application in quality control of data, goodness tests, distribution tests, fundamentals of multivariate statistics in exploration including bivariate statistics, covariance, correlation coefficient, linear regression, non-linear regression, levels of significance of regression line, probability distributions, multi-regression processing, application of multivariate regression in geochemical exploration, Discriminate analysis, methods based on eigenvalues including PCA and FA, R-Mode and Q-Mode Factor Analysis, clustering methods, distance as similarity criteria, dendrogram, comparative analysis, characteristic analysis, application of preprocessing in data quality and data accuracy, outlier detection, Cohen maximum likelihood estimation, removing syngenetic effects of data using PCA and fuzzy logic methods, geochemical anomaly delineation such as structural and non-structural methods, techniques of mapping of exploratory data, estimation methods in exploration including a review of the traditional methods of reserve estimation and its difference from geostatistical estimations as kriging, geostatistical simulation. LU method and its application in evaluating the mineral potential, fuzzy logic and its application in exploration, distance and thickness fractal estimations of mineralized veins, application of neural networks in mineral inventory estimation, estimation of spatial data structure by Fry, multi-criteria decision-making methods including EMV.