Study information

Sampling Theory and Data Analysis - 2019 entry

MODULE TITLESampling Theory and Data Analysis CREDIT VALUE15
MODULE CODECSMM433 MODULE CONVENERProf Hylke J Glass (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 3 0 0
Number of Students Taking Module (anticipated) 10
DESCRIPTION - summary of the module content

The module gives graduates from a range of disciplines the opportunity to study sampling theory and statistics and data analysis. This module will provide valuable knowledge of techniques which are applicable across a range of disciplines and are of vital importance for designing experiments, understanding and interpreting experimental data and accurately assessing the performance of mineral processing plants. This will be invaluable for the individual research project undertaken as a part of the MSc Mineral Processing programme and in later life.

When linked to module CSMM434, it forms part of the specialist training for the MSc in Minerals Processing.

Students are expected to have a basic knowledge of statistics and mathematics to fully engage with this module. Guidance on appropriate self-study to improve knowledge in these areas can be given if desired.

 

 

AIMS - intentions of the module

This module has been designed to develop an understanding of sampling and data analysis. The aim is to give students an appreciation of the importance of careful sampling and teach techniques and approaches to assess whether samples are representative of a whole bulk and maximise this representivity. Further to this the module aims to teach a range of tools for data analysis and instil confidence in applying these to experimental data. These are vital skills for use in future employment.

 

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

 

Module Specific Skills and Knowledge

1. Understand basic statistical concepts such as statistical distributions, bias, uncertainty and error.

2. Understand the link between sample variance and sample size and how appropriate sample masses can be estimated to minimise error.
 
3. Describe suitable procedures and techniques for sampling material in the context of the minerals processing industry.
 
4. Apply data analysis tools to the interpretation of experimental data.

 

Discipline Specific Skills and Knowledge

5. Apply appropriate mathematical methods, scientific principles and computer based methods to the modelling, analysis and solution of practical engineering problems.
 
6. Work safely in laboratory, workshop environments etc., and promote safe practice.

 

Personal and Key Transferable / Employment Skills and Knowledge

7. Sort, manipulate and present data in a way that facilitates effective analysis and decision making.
 
8. Communicate effectively and persuasively using the full range of currently available methods.

 

SYLLABUS PLAN - summary of the structure and academic content of the module

Sampling theory topics

  1. Statistical distributions: types, moments, and statistics
  1. Concepts: uncertainty and bias
  1. Derivation of sampling variance equation
  1. Variance in the sampling value chain
  1. Inference based on sample analysis
  1. Sample size determination
  1. Optimisation of sample size
  1. Types of sampling techniques

 

Data analysis topics

  1. Reconciliation: obtaining a closed mass balance
  1. Regression modelling: least squares estimation
  1. Analysis of variance (ANOVA)
  1. Clustering: principle component analysis, dendrogram
  1. Bayesian prediction
  1. Fuzzy logic
  1. Neural networks

 

Health and Safety engagement

  1. Health and safety implications related to sampling of different types of minerals in laboratory and industrial settings are discussed

 

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 30 Guided Independent Study 114 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning & teaching activities 12 Lectures
Scheduled learning & teaching activities 18 Computer Tutorials
Guided independent study 114 Private Study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Prior knowledge and skills e-assessment 100 word equivalent   Electronic supported by verbal if required

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Sampling exercises and calculations 50 2,000 word equivalent 1-2, 5-7 Electronic or written feedback
Data analysis exercises and calculations 50 2,000 word equivalent 3-7 Electronic or written feedback

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Coursework New assignment 1-7 Ref/def period

 

RE-ASSESSMENT NOTES
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

ELE: http://vle.exeter.ac.uk/

 

Web based and Electronic Resources:

http://app.knovel.com

 

Other Resources:

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Napier-Munn, T.J., Statistical methods for mineral engineers - how to design experiments and analyse data Julius Kruttschnitt Mineral Research Centre, Indooroopilly, Queensland, Australia 2014
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 12th February 2019 LAST REVISION DATE Tuesday 12th February 2019
KEY WORDS SEARCH Sampling theory; sampling statistics; data analysis; statistical distributions; bias; uncertainty; error; sampling variance theory; sample size optimisation; sampling techniques; data reconciliation; regression analysis; ANOVA; principle components...

Please note that all modules are subject to change, please get in touch if you have any questions about this module.