For Mineral Engineers __full__: Statistical Methods

The primary resource for this topic is the book Statistical Methods for Mineral Engineers: How to Design Experiments and Analyse Data Professor Tim Napier-Munn

Statistical Methods for Mineral Engineers heads for third reprint Statistical Methods For Mineral Engineers

• Moving range chart for plant assays.
• Detect autocorrelation (common in mill feed).
• Example: CUSUM chart to catch a slow drift in zinc tailings.

• Book: Mineral Processing Design and Operations (Gupta & Yan) — Chap on statistics.
• Paper: "Gy’s sampling theory: a practical guide" (Pitard, 2019).
• Software: R (packages ggplot2, DoE.base, qcc) — free and powerful.
• Course: “Data Analysis for Mineral Engineers” (look up SAIMM or SME short courses).

confirmation bias

This book acts as a safeguard against . Engineers naturally want to see improvements in their circuits. By applying the rigorous statistical validation methods detailed in this book, engineers can present plant modifications with confidence, backed by probability rather than intuition. The primary resource for this topic is the

Mineral engineering is inherently a discipline of uncertainty. Unlike manufacturing, where raw materials are consistent, mining deals with natural deposits that vary wildly in grade, geometry, and geotechnical properties. Statistical methods provide the tools to quantify this uncertainty, optimize processes, and manage risk. Moving range chart for plant assays

$$ \sigma^2_FSE = \frac1M_S \left( \fracf g \beta d^3c \right) $$

Decisiveness

: Reviewers at Informit highlight its ability to translate vague observations into "clear demonstrable facts," supporting value-adding decisions.

Statistical Methods for Mineral Engineers is the title of a highly regarded book by Professor Tim Napier-Munn , published through the Julius Kruttschnitt Mineral Research Centre (JKMRC)