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MATH4071: Topics in Statistics IV

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box, and this may change from year to year, due to, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback. Current modules are subject to change in light of the ongoing disruption caused by Covid-19.

Type Open
Level 4
Credits 20
Availability Not available in 2023/24
Module Cap
Location Durham
Department Mathematical Sciences

Prerequisites

  • Statistical Methods III (MATH 3051).

Corequisites

  • None.

Excluded Combinations of Modules

  • Topics in Statistics III (MATH 3361).

Aims

  • To provide a working knowledge of the theory, computation andpractice of a number of specialised statistical tools, complementingStatistical Methods III.

Content

  • Likelihood-based inference
  • Generalised linear models
  • Log-linear modelling of contingency tables
  • Advanced topic: one of multivariate analysis, time seriesanalysis, medical statistics.
  • Reading material in an advanced area of statistics chosenby the lecturer.

Learning Outcomes

Subject-specific Knowledge:

  • By the end of the module students will:
  • be aware of a wide range of applicable statisticalmethodology.
  • have a systematic and coherent understanding of the theory,computation and application of the mathematics underlying thestatistical topics studied.
  • have acquired a coherent body of applicable knowledge onlikelihood methods as a general approach to inference.
  • have acquired a coherent body og knowledge of generalisedlinear methods and log-linear modelling.
  • have a knowledge and understanding of a substantial topic inan advanced area of statistics obtained by independent study.

Subject-specific Skills:

  • In addition students will have specialised mathematicalskills 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 theapplication of the theory to practical examples.
  • Computer practicals consolidate the studied material andenhance practical understanding.
  • Assignments for self-study develop problem-solving skills andenable students to test and develop their knowledge andunderstanding.
  • Formatively assessed assignments provide practice in theapplication of logic and high level of rigour as well as feedback forthe students and the lecturer on students' progress.
  • The end-of-year examination assesses the knowledge acquiredand the ability to solve predictable and unpredictableproblems.

Teaching Methods and Learning Hours

ActivityNumberFrequencyDurationTotalMonitored
Lectures402 per week for 20 weeks (omitting two slots) and 2 in term 31 Hour40 
Computer Practicals2In unused lecture slots in first two terms1 Hour2 
Problems Classes8Four in each of terms 1 and 21 Hour8 
Preparation and Reading150 
Total200 

Summative Assessment

Component: ExaminationComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Written examination3 hours100 

Formative Assessment

More information

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