Practical Geostatistics 2000 - 1: Classical Statistics

Areas of Study: Exploration and Geology

Qualifies for CMS

Premium Peer-Reviewed

Qualifies for Certification

This is the first of two courses based on over 40 years of teaching statistics and geostatistics to mining engineers, geologists, hydrologists, soil scientists and climatologists, plus the occasional geographer, pattern recognition expert, meteorologist, statistician, and computer scientist. It is intended for people with problems to be solved that can be assisted by a geostatistical approach. This course covers the statistical groundwork for the geostatistical techniques presented in the second course. *** This is a premium course which has been peer-reviewed by a committee appointed by the Canadian Institute of Mining, Metallurgy and Petroleum (CIM) and the Society for Mining, Metallurgy and Exploration (SME).

Enroll for Access to All Online Courses

Enrollees have access to all self-paced online courses.

Certification available for Not Available
  • Audience Level:
  • Professional
  • Enrollment:
  • Required
  • Duration:
  • 25 hours

Course Summary

Introduction

Practical Geostatistics 2000 - 1: Classical Statistics is the first of a set of two courses. The companion course is Practical Geostatistics 2000 - 2: Spatial Statistics.

These courses are based on over 40 years of teaching statistics and geostatistics to mining engineers, geologists, hydrologists, soil scientists, climatologists, plus the occasional geographer, pattern recognition expert, meteorologist, statistician, and computer scientist. Even, on one occasion, an accountant. Over those years, we have endeavoured to pare away all extraneous mathematics and concentrate on intuitive derivations where possible.

Readers interested in rigorous mathematical proofs are urged to stop here and turn to the more theoretically based material (a comprehensive bibliography is included). This course is not intended to turn out fully-fledged geostatisticians. It is intended for people with problems to be solved which can be assisted by a geostatistical approach.

To benefit from this course you need to be fairly comfortable with basic algebra. That is, with the notion of using symbols as shorthand for longer statements. We have worked hard to bring you consistent notation throughout the course. Where notation is out of our control, we explain carefully what each symbol stands for and try not to use that symbol for anything else.

Calculus—differentiation and integration—is discussed at various points in the text. The reader is not expected to do any calculus (as such) but is expected to know that the differential of x squared is 2x. The only other complication is the frequent use of simultaneous equations. We tend not to use matrix algebra in this course but will give the matrix form after explanations have been given in simple algebra. For example, linear regression is easier to understand if developed with algebra, but very simple to implement in spreadsheets if matrices are used.

If we haven't scared you off yet, be reassured by the fact that all the analyses are illustrated with real data sets in full worked examples. Data sets and software can be downloaded from Ecosse Geostatistics. There are also exercises for you to try. Answers are available for you to check your results. Most of these exercises have been collected and used in classes or examinations at Final (Senior) Year and Master's levels.

It is our own fundamental regret that this course cannot contain the jokes, anecdotes and sheer fun that we have giving the course in person. We do advise you, however, to keep your sense of humour and common sense at all times while taking this course.The principal topics covered by this course include...

  • Why a Statistical Approach?
  • The Normal (Gaussian) Distribution
  • The Lognormal Distribution (and Variants)
  • Discrete Statistics
  • Testing Hypotheses
  • Relationships
The course comprises 24 viewing sessions, each of approximately 60 minutes duration, plus supporting figures, tables, worked examples, references and appendices, and interactive reviews that confirm your achievement of the learning objectives.The above picture is attributed to USACE HQ.

Learning Outcomes

  • Identify and apply the basic descriptive and graphical tools of statistics including measures of central tendency, measures of spread and variability, histograms, bar charts and box plots.
  • Identify and apply the Normal probability distribution and its parameters and properties including mean, standard deviation, confidence intervals and selection calculations.
  • Identify and apply the Lognormal probability distribution and its parameters and properties including mean, standard deviation, confidence intervals and selection calculations.
  • Identify and apply discrete probability distributions including Binomial, Poisson, geometric and mixed distributions.
  • Identify and apply the principles of hypothesis testing including single sample tests, two sample tests and paired tests.
  • Identify and apply the principles of data relationships including straight line, multi-variable and curved relationships, and polynomial trend surface analysis.

Recommended Background

  • Familiarity with algebra, simple calculus and basic statistics.

Isobel Clark

Dr. Isobel Clark provides consultancy through Geostokos Limited, almost exclusively in the field of mineral resource and reserve estimation, most often at feasibility or even pre-feasibility stage. She has a B.Sc. (1969) in Pure and Applied Mathematics from Strathclyde University, an M.Sc. (1970) in Biometrics from University of Reading, a Ph.D. (1979) in Engineering from University of London, and a Diploma in Educational Broadcasting (1980) from University of York. She is a Fellow of the Royal Statistical Society, an Associate Member of the Biometrics Society, a Member of the Institution of Mining and Metallurgy, a Member of the International Association of Mathematical Geology, a Fellow of the Institute of Mathematics and its Applications, Associate Member of the Society of Mining Engineers of AIME, a Member of the Geostatistical Association of South Africa, and a Fellow of the South African Institution of Mining and Metallurgy.

Dr. Clark has taught at the Royal School of Mines at Imperial College, the Department of Mining Engineering at University of the Witwatersrand, and Camborne School of Mines at University of Exeter. She is the author of Practical Geostatistics (1979), co-author of Practical Geostatistics 2000 with William Harper, and author of numerous technical papers and short courses. Her major area of investigative study has been in the application of the "Theory of Regionalised Variables" in ore reserve estimation and other appropriate fields.

William Harper

William Harper currently teaches mathematical sciences at Otterbein College in Columbus, Ohio. He has a BS in Computer Science Engineering (1974), an MS in Statistics (1976), and a PhD in Industrial and Systems Engineering (1984) from The Ohio State University. He is a Fellow of the American Society for Quality.

William Harper is a Licensed Professional Industrial Engineer in the State of Ohio. He is also a Certified Quality Engineer, a Certified Reliability Engineer, and a Certified Quality Auditor with the American Society for Quality. He is co-author of the book Practical Geostatistics 2000 with Isobel Clark. His current interests include Geostatistics, Simulation, Operations Research, Quality Management and Statistics.