# Unit information: Multivariate Analysis in 2021/22

Unit name Multivariate Analysis MATH30510 10 H/6 Teaching Block 2C (weeks 13 - 18) Dr. Cho Not open MATH10013 Probability and Statistics, and MATH10015 Linear Algebra None School of Mathematics Faculty of Science

## Description including Unit Aims

Unit Aims

To present various aspects of multivariate analysis, covering data exploration, modeling and inference.

Unit Description

Multivariate analysis is a branch of statistics involving the consideration of objects on each of which are observed the values of a number of variables. A wide range of methods is used for the analysis of multivariate data, both unstructured and structured, and this course will give a view of the variety of methods available, as well as going into some of them in detail.

Interpretation of results will be emphasized as well as the underlying theory.

Multivariate techniques are used across the whole range of fields of statistical application: in medicine, physical and biological sciences, economics and social science, and of course in many industrial and commercial applications.

Relation to Other Units

As with the units Linear and Generalized Linear Models and Time Series Analysis, this course is concerned with developing statistical methodology for a particular class of problems.

Applications will be implemented and presented using the statistical computing environment R (used in Probability and Statistics).

## Intended Learning Outcomes

Learning Objectives

To gain an understanding of:

• Dimensional reduction and visualisation of high-dimensional datasets;
• Structured and unstructured learning approaches, including classification and clustering;
• Approaches based on notions of similarity/dissimilarity;
• Implementation in the statistical computing environment R.

Transferable Skills

Self assessment by working examples sheets and using solutions provided.

## Teaching Information

The unit will be taught through a combination of

• synchronous online and, if subsequently possible, face-to-face lectures
• asynchronous online materials, including narrated presentations and worked examples
• guided asynchronous independent activities such as problem sheets and/or other exercises
• synchronous weekly group problem/example classes, workshops and/or tutorials
• synchronous weekly group tutorials
• synchronous weekly office hours

## Assessment Information

Assessed coursework (10%) and exam (90%)

Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.

## Resources

If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.

If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. MATH30510).

How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours of study to complete. Your total learning time is made up of contact time, directed learning tasks, independent learning and assessment activity.