Unlike laboratory experiments, plant data is autocorrelated: today’s feed grade is correlated with yesterday’s. Standard t-tests or regression (which assume independence) give misleading p-values.
Statistical Methods for Mineral Engineers is not just a math book; it is a risk management tool. Its defining feature is the translation of statistical theory into a decision-making framework for high-throughput, variable-heavy mineral processing environments. Statistical Methods For Mineral Engineers
Statistical Methods for Mineral Engineers heads for third reprint Its defining feature is the translation of statistical
[Your Name/Organization] specializes in applied statistics for mineral processing and geometallurgy. For further reading, see Gy’s Sampling Theory (Pitard, 2019), Statistics for Mining Engineers (Srivastava, 2016), and Design and Analysis of Experiments (Montgomery, 2020). see Gy’s Sampling Theory (Pitard
Modern metallurgical accounting uses minimization of weighted sum of squares to adjust measurements so they obey the conservation of mass (tonnage and metal).
Statistical methods help quantify the inherent "noise" in mineral processing: Error Propagation