Includes bibliographical references and index.

Chapter 1. Introduction -- Chapter 2. The presentation of data -- Chapter 3. Uncertainty in data: the nature and consequence of experimental error -- Chapter 4. Comparing quantities -- Chapter 5. Building and evaluating regression models -- Chapter 6. More about regression models -- Chapter 7. Designing efficient experiments -- Chapter 8. Designing, running and analysing plant trials -- Chapter 9. The analysis of time series data -- Chapter 10. Multivariate analysis -- Chapter 11. Performance monitoring and optimisation -- Chapter 12. Statistics for chemists and mineralogists -- Chapter 13. Other topics of interest -- Chapter 14. A roadmap for collecting and analysing data.
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.