Dissertation: Reception of the Classical World (CLAS0126)

The dissertation builds on expertise gained across the taught programme, especially the core compulsory module Approaches to the reception of the Classical World. Students are also expected to attend relevant parts of the MA Classics: Research methods module held in autumn term and the various workshops throughout the year on dissertation research, presentations, and writing. A dissertation of 12,000 words on an aspect of the reception of the classical world.

A series of workshops held in the autumn term, with the aim of introducing MA students to the range of topics, sources, methodologies and research techniques...

MA Classics: Research Methods and Dissertation (CLAS0051)

A series of workshops held in the autumn term, with the aim of introducing MA students to the range of topics, sources, methodologies and research techniques of classical scholarship. The workshops are essential for preparing students to participate in classes and write papers, especially the dissertation. The final title of the dissertation to be confirmed by 15 June and submitted by 15 September. Word length is 12,000 words.

Students are also expected to attend the various workshops throughout the year on dissertation research, presentations, and writing.

This module has been developed to give students a foundational knowledge of the main imaging modalities used in diagnostic and research neuroimaging.
At the...

Physical Sciences Module 2: Imaging Modalities (ANIM0008)

Aims - This module aims to provide an introduction to linear statistical modelling and to the analysis of variance with emphasis on ideas, methods,...

Linear Models and the Analysis of Variance (STAT0006)

Aims - This module aims to provide an introduction to linear statistical modelling and to the analysis of variance with emphasis on ideas, methods, applications and interpretation of results. It is primarily intended for second year students registered on the undergraduate degree programmes offered by the Department of Statistical Science, or jointly with other departments. It also serves as a core module for students taking the mathematics and statistics stream as part of the Natural Sciences degree.

Objectives - On successful completion of the module, a student should have an understanding of the basic ideas underlying multiple regression and the analysis of variance; be able to build and interpret appropriate linear regression models; understand the assumptions underlying these analyses and know how to check their validity.

Applications - Linear models and the analysis of variance (ANOVA) are two basic and powerful statistical tools to model and analyse the relationship between random variables, and thus are widely used in almost all of classical and modern statistical practice. Their use exemplifies the modern, model-based approach to statistical investigations, and provides the foundations for more advanced techniques that may be required for the study of complex systems arising in areas such as economics, natural and social sciences and engineering as well as in business and industry.

Content - Multiple regression: model fitting by least squares, model assessment via residuals, and model selection. Analysis of variance in relation to multiple regression will also be discussed. Emphasis will be placed on ideas, methods, practical applications, interpretation of results and computer output, rather than on detailed theory.

Key texts - Available from ReadingLists@UCL.