MAS6002 Statistical Laboratory

Note: This is an old module occurrence.

You may wish to visit the module list for information on current teaching.

Both semesters, 2019/20 20 Credits
Lecturer: Dr Kevin Walters uses MOLE Timetable Reading List
Aims Outcomes Teaching Methods Assessment Full Syllabus

This module starts in Intro Week with a speedy review of the basic background expected for the MSc. The module then introduces the basics of the R language, demonstrates how to use and write functions in R, introduces some programming tools, shows how R can be used for Monte Carlo techniques and how results can be graphically displayed.

There are no prerequisites for this module.
No other modules have this module as a prerequisite.

Outline syllabus

  • Introductory theory (Probability, Likelihood, Basic Statistics)
  • Introduction to the R language.
  • Writing R functions.
  • Programming in R.
  • Monte Carlo Simulation in R.
  • Researching modern statistical techniques: independent study.

Office hours

Invite me to a meeting on my Google calendar whenever I am free.


  • To review basic theoretical material.
  • To enable students to write basic R code.
  • To give practice in writing functions in R.
  • To help students to write longer programs.
  • To illustrate how R can be used to implement a range of Monte Carlo techniques
  • To give practice in applying Monte Carlo methods and interpreting results from them.
  • To give practice in creating reproducible work using R Markdown.

Learning outcomes

  • demonstrate capability in basic introductory theory;
  • write simple functions and programs in R;
  • develop skills in writing longer, more complex R programs;
  • understand how Monte Carlo simulations can be implemented in R
  • find out independently about further areas of methodology;
  • prepare technical documents with R Markdown.

Teaching methods

Examples classes and computer practice in the PC Laboratories; on-line discussion. Feedback on assignments. Group work. Student presentations.

No lectures, no tutorials, 14 practicals


Four continuously assessed assignments (one set of problems, two reports, and one presentation).

Full syllabus

N.B `sessions' are 1.5 or 2hrs.

  • Recap of probability, likelihood, basic statistics (9 hours)
  • Introduction to the R language(5 hours).
  • Writing R functions (5 hours).
  • Monte Carlo Simulation in R (5 hours).
  • Programming in R (5 hours).
  • Researching modern statistical techniques: independent study.

Reading list

Type Author(s) Title Library Blackwells Amazon
B Grolemund, G. Hands-on Programming with R Blackwells Amazon
B Grolemund, G. and Wickham, H. R for Data Science Blackwells Amazon
B Xie, Y., Allaire, J.J. and Grolemund G. R Markdown: The definiitive Guide Blackwells Amazon

(A = essential, B = recommended, C = background.)

Most books on reading lists should also be available from the Blackwells shop at Jessop West.

Timetable (semester 1)

Mon 10:00 - 10:50 lecture   Diamond Computer Room 1 / Room 201