Probability graphs display a data set as a cumulative distribution. The most significant use of probability graphs applied to mineral exploration data is in the recognition of the number of populations in a data set, and the partial or complete partitioning of individual values into their respective groups or populations. The significance of the resulting groupings or populations of data must be interpreted.
Interpreting probability graphs is largely a matter of understanding the implications of the patterns that result when data sets are plotted. These implications are not always fully appreciated, and in some cases, the conclusions drawn from the probability graphs are incorrect.
In this course, you will learn how probability graphs can supplement analyses done using histograms, and how this can be beneficial when interpreting mineral exploration data. The course explains data distributions and populations. You will learn that probability graphs are an easy way to estimate the forms of distributions and their parameters. They are a useful tool to present and analyze many types of numeric data that are the product of mineral exploration programs. The course also highlights general advantages, but also limitations of using probability graphs, and provides useful procedure tips to draw up the graphs.
Note that this course assumes a working knowledge of simple statistical concepts (e.g. arithmetic mean, variance, standard deviation, normal density distribution, etc.). The course content uses a clear-cut, idealized approach illustrated by real life practical examples used throughout the mining industry. The Appendix includes a variety of interpretations of published probability graphs with alternate interpretations and discussion on the analytical approaches used by the original publications.
The course consists of 17 viewing sessions of 45–60 minutes each with supporting figures, tables, and multiple choice course reviews. Course duration is equivalent to approximately 16 hours of viewing content.