## ST3CTS-Computational Techniques in Statistics

Module Provider: Mathematics and Statistics
Number of credits: 10 [5 ECTS credits]
Level:6
Terms in which taught: Autumn term module
Pre-requisites: ST1PD Probability and Distributions ST1SIM Statistical Inference and Modelling or ST2MS Medical Statistics
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Module version for: 2016/7

Module Convenor: Miss Hannah Fairbanks

Summary module description:
This module provides an introduction to the area of computational statistics. The exponentially increasing power of computers has enabled statisticians to approach problems with a much more flexible attitude, for example free from distributional assumptions. Amongst other topics this module will explore the ability to use computers to quickly generate large samples to explore distributions using simulation rather than analytically, and consider computationally intensive regression applications to smooth data for which standard models are not appropriate.

Aims:
This module aims to give students an understanding of the statistical techniques made feasible by the power of computers. It should provide them with the theoretical and programming skills to tackle problems using the techniques listed in the outline content below.

Assessable learning outcomes:
On completion of this module it is expected that the students will have / will have developed:
- an appreciation for the need/uses of computationally intense statistical techniques
- an understanding of the principals behind the computational statistics techniques covered
- an ability to apply those techniques to real life data
- an understanding of the appropriate results to present and interpret for each of the techniques covered
- further skills in statistical computing
- the skills needed to work as part of a team
- research skills.

Outline content:
- Simulation, random number generation, rejection sampling.
- Resampling methods: bootstrap, balanced bootstrap, jacknife.
- Introduction to Monte Carlo methods, including Monte Carlo Integration.
- Monte Carlo, permutation and randomisation tests.
- Kernel density estimation.
- Smoothing techniques: LOESS, splines.

The statistical software package R will be used to illustrate the topics listed above, and students will be expected to complete computer practicals and assignments in R.

Brief description of teaching and learning methods:
Lectures, supported by computer practicals.

Contact hours:
 Autumn Spring Summer Lectures 16 Practicals classes and workshops 8 Guided independent study 76 Total hours by term 100.00 Total hours for module 100.00

Summative Assessment Methods:
 Method Percentage Written exam 50 Set exercise 50

Other information on summative assessment:
Two assignments and one examination.

Formative assessment methods:
Computer practicals (supported and self-study).

Penalties for late submission:
The Module Convenor will apply the following penalties for work submitted late, in accordance with the University policy.

• where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
• where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

• The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Length of examination:
2 hours.

Requirements for a pass:
A mark of 40% overall.

Reassessment arrangements:
One examination paper of 2 hours duration in August/September - the resit module mark will be the higher of the exam mark (100% exam) and the exam mark plus previous coursework marks (50% exam, 50% coursework).