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MA50260: Statistical modelling

Follow this link for further information on academic years Academic Year: 2019/0
Further information on owning departmentsOwning Department/School: Department of Mathematical Sciences
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Masters UG & PG (FHEQ level 7)
Further information on teaching periods Period:
Semester 2
Further information on unit assessment Assessment Summary: EX 100%
Further information on unit assessment Assessment Detail:
  • Examination (EX 100%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites: Before taking this module you must take XX50215
Further information on descriptions Description: Aims:
To understand and apply linear, generalised linear and mixed effect models (GLMM).

Learning Outcomes:

* choose an appropriate generalised linear mixed model for a given set of data;
* fit this model, select terms for inclusion in the model and assess the adequacy of a selected model;
* make inferences on the basis of a fitted model and recognise the assumptions underlying these inferences and possible limitations to their accuracy.

Skills:
Problem solving (T, F, A), computing (T, F, A), written communication (F, A).

Content:
Multiple linear regression: inference techniques for the general linear model, diagnostics, transformation and variable selection. Generalised linear models: exponential family of distributions and inference procedures. Logistic regression and log-linear models. Mixed effect models: hierarchical and grouped data, nested and crossed designs.
Further information on programme availabilityProgramme availability:

MA50260 is Compulsory on the following programmes:

Department of Computer Science

Notes: