Introduction to computational statistics provides an opportunity for the student to witness theoretical techniques that they have been taught being applied to experimental data.  This is achieved through the application of a statistical package to practical examples. The course material covers elementary statistical methods to more advanced techniques used to interrogate and understand relationships contained in data. 

Prerequisite Modules: MA1501 Statistical Inference

Corequisite Modules: MA2500 Foundations of Probability and Statistics

• analyse experimental data
• perform statistical manipulations
• apply statistical concepts essential in the field of data analysis

22 - 50 minute lectures

11 - 50 minute (computer lab) tutorial classes

Some handouts will be provided in hard copy or via Learning Central, but students will be expected to take notes of lectures

Practical training in application of statistical packages will be provided in computer tutorials.

Students are also expected to undertake at least 50 hours private study including preparation of worked solutions and preparation for work in (computer) tutorials classes.

Skills:

Working with statistical software, graphical representation of multivariate data, statistical model building, applying computationally standard statistical techniques such as parameter estimation and hypotheses testing.

Transferable Skills:

General computer skills – will become familiar with the application of statistical software to interrogate data.

Time management skills – learn how to timetable work such that deadlines are met when working independently.

Data analysis – will be able to view statistical principles from a practical point of view.

Formative assessment is carried out by means of regular (computer lab) tutorial exercises.  Feedback to students on their solutions and their progress towards learning outcomes is provided during lectures, and tutorial classes.  

The summative assessment consists of two in-course components:

The first, which is weighted at 15%, is a coursework exercise based on utilising the packages to analyse experimental data and apply principles taught in lectures.

The other is a class test at the end of the module. This will be of 2 hours duration and will be held in the computer laboratories.  The test will be worth 85% of the assessment of the module and will involve applying relevant statistical techniques taught in the lectures and tutorials to analyse/generate experimental data. The candidates’ understanding of the techniques will then be demonstrated through answering questions relating to the data.

• Data Analysis

• Univariate Statistical Techniques (such as confidence intervals and hypothesis testing)

• Multivariate Statistical Techniques (such as regression)

• Introduction to Statistical Programming

Applied multivariate data analysis, Everitt, B. S., &Dunn, G., Edward Arnold, 1991

Computer-aided multivariate analysis (3rd Ed), Afifi, A. A., & Clark, V., Chapman & Hall, 1996.

Applied multivariate statistical analysis (4th Ed), Johnson, R. A., Wichern, D. W., Prentice Hall, 1998

Introductory Statistics with R (2nd Ed), Dalgard, P., Springer, 2008

A First Course in Statistical Programming, Braun, W. J., Murdoch, D. J., 2007