This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.

Undergraduate Handbook 2010-2011 (archived)

Module MATH3051: STATISTICAL METHODS III

Department: Mathematical Sciences

MATH3051: STATISTICAL METHODS III

Type	Level	Credits	Availability	Module Cap	Location
Open	3	20	Available in 2010/2011	None.	Durham

Pre-requisites

Linear Algebra II (MATH 2021) AND Statistical Concepts II (MATH2041).

Corequisites

None.

Excluded Combinations

None.

Aims

To provide a working knowledge of the theory, computation and practice of multivariate statistical methods, with focus on the linear model.

Content

Introduction to statistical software for data analysis.
Multivariate normal distribution.
Multivariate analysis, including principal component analysis.
Regression: general linear model, diagnostics, influence, transformations, variable selection, weighted least squares, analysis of variance, factorial experiments.

Learning Outcomes

Subject-specific Knowledge

By the end of the module students will:
be able to solve novel and/or complex problems in Statistical Methods.
have a systematic and coherent understanding of the theory and mathematics underlying the statistical methods studied.
be able to formulate a given problem in terms of the linear model and use the acquired skills to solve it.
have acquired a coherent body of knowledge on regression methodology, based on which extensions of the linear model such as generalized or nonparametric regression models can be easily learnt and understood.

Subject-specific Skills

In addition students will have specialised mathematical skills in the following areas which can be used with minimal guidance: Modelling, Computation.

Key Skills

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
Computer practicals consolidate the studied material and enhance practical understanding.
Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
Formatively assessed assignments provide practice in the application of logic and high level of rigour as well as feedback for the students and the lecturer on students' progress.
The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems..

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total Hours
Lectures	38	2 per week for 18 weeks in first two terms and 2 weeks in third term	1 Hour	38
Computer Practicals	2	In unused lecture slots in first two terms	1 Hour	2
Preparation and Reading				160
Total				200

Summative Assessment

Component	Component Weighting	Element	Length/Duration	Element Weighting	Resit Opportunity
Examination	100%	Written examination	3 hours and 15 minutes, including 15 minutes reading time	100%

Formative Assessment

Four written or electronic assignments to be assessed and returned. Other assignments are set for self-study and complete solutions are made available to students.

Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.