KC6029 - Advanced Statistical Methods

You will learn on this module via a combination of lectures and laboratory sessions involving the use of R statistical software. Classes will be scheduled in our modern computer laboratories enabling you to apply the techniques presented in the lecture part of the session and, in this way, gain a deeper understanding of the material and develop your practical skills. The bespoke laboratory sessions will take place every week.

Assessment involves an assignment and a laboratory examination. You will receive both written and oral feedback from the assignment. Oral feedback will be given concurrently during the laboratory sessions. Further to this, you will also receive exam feedback after the end of first semester, particularly relevant if you are on the MMath degree programme.

In addition, we operate an open door policy where you can meet with your module tutor to seek further advice or help if required. Your ability to analyse germane data and convey results in a concise report will be assessed via an individual assignment with individualised data, and your ability to choose the appropriate analysis for a range of problems will be tested in a laboratory exam at the end of the module.

You will be supported academically chiefly through participation in the hands-on laboratory sessions, where you will be analysing data with appropriate software. This provides you with the opportunity to receive support on both the theory and technical components of the module simultaneously as well as offering experience of conditions representative of the final assessment.

You can engender further feedback and discussion with the teaching team at any time through our open door policy. In addition, all teaching materials, selected R commands, and supplementary material (such as interesting articles) are available through the e-learning portal.

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

Knowledge & Understanding:
1. Use an appropriate statistical package to apply multivariate and regression techniques
2. Appraise problems in order to select and apply the appropriate method for statistical analysis

Intellectual / Professional skills & abilities:
3. Develop appropriate multivariate and regression techniques to build models for data analysis, implementing such models using statistical software.
4. Critically evaluate statistical models based on real-life data

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
5. Research and cite information appropriately, in order to analyse data and present findings effectively

SUMMATIVE
1. Coursework (30%) – 1, 2, 4, 5
2. Lab-based examination (70%) – 1, 2, 3, 4

FORMATIVE
Formative assessment will be available on a weekly basis in the laboratory sessions via normal lecturer-student interactions, allowing them to extend, consolidate and evaluate their knowledge.

Formative feedback will be provided on your work, and errors in understanding will be addressed reactively using one-to-one discussion. Solutions for laboratory tasks will be provided after you have attempted the questions, allowing you to receive feedback on the correctness of your solutions and to seek help if matters are still not clear.

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This module is designed to introduce advanced statistical methods with a dual focus on multivariate techniques and applied regression models. These topics will develop methods to analyse large, complex and multi-faceted data with real-life case studies drawn from finance, medicine and sport used to illustrate. Skills in using statistical software will be developed throughout the module to analyse data. In part one of the module the primary focus is on the data reduction techniques of principal components analysis and linear discriminant analysis. Regression models form the second part of the module, considering cases where we have multiple explanatory variables, indicator variables, polynomial and non-Gaussian relationships. Assessment of the module is by one individual assignment (30%) and one formal laboratory examination (70%). The module is designed to provide students with a useful preparation for employment or postgraduate study in an applied statistical environment where knowledge of appropriate software is key.