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Statistical Methods for Mineral Engineers - How to Design Experiments and Analyse Data
By T.J. Napier-Munn
Mineral Processing for Nano-Scientists was conceived as a short account to introduce the field of extracting metals from ores to nano-scientists since they are the final users of these materials. In 170 pages the areas of comminution, separation of minerals, pyro-, hydro-, and electrometallurgy are briefly outlined. The book is fully illustrated.
Written by a mineral engineer for mineral engineers, and packed with real world examples, this book de-mystifies the statistics that most of us learned at university and then forgot. It shows how simple statistical methods, most of them available in Excel, can be used to make good decisions in the face of experimental uncertainty. Written in accessible language, it explains how experimental uncertainty arises from the normal measurement errors and how statistics provides a powerful methodology to manage that uncertainty. It assumes only that the readers are numerate, can use Excel, and want to do a better professional job. It is aimed squarely at mineral engineers and allied professionals (such as chemists) on the mine site, in head office, in engineering and supply companies and in universities.
Most of the examples are illustrated in Excel but Minitab is also used for advanced techniques. The book includes over 100 Excel and Minitab hints. Example spreadsheets can be downloaded from the JKMRC and JKTech websites (see book for URL).
The book is based on the authorís world-renowned professional development course on statistics for mineral engineers, but covers much more material. Topics include:
- the presentation of data - charts, tables and PowerPoint.
- uncertainty in data - precision, accuracy, the normal distribution, sources of error.
- comparing quantities using hypothesis tests such as the t-test, F-test, chi-square test, ANOVA, non-parametric tests.
- modelling using regression analysis, including linear, non-linear and weighted regression.
- designing and analysing efficient experiments and plant trials.
- time series analysis, including variograms and time series models.
- multivariate analysis (PCA, clustering, binary logistic regression, MANOVA).
- performance monitoring and optimisation, including statistical process control and EVOP.
- statistics for chemists and mineralogists, mass balancing, sampling (Gy theory).
- Monte Carlo and bootstrap methods
- a selection scheme to choose the appropriate statistical tool for the job in hand.
Click here to visit the JKMRC website to order the book.
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