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Unit information: Statistics 2 in 2021/22

Unit name Statistics 2
Unit code MATH20800
Credit points 20
Level of study I/5
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Professor. Anthony Lee
Open unit status Not open
Pre-requisites

MATH10013 Probability and Statistics

Co-requisites

None

School/department School of Mathematics
Faculty Faculty of Science

Description

Lecturers: Anthony Lee and Skevi Michael

Unit Aims

To develop the theory and practice of basic statistical inference, and statistical calculation.

Unit Description

Statistics is about inference under uncertainty, ie in situations where deductive logic cannot give a clearcut answer. In these situations our decisions must be assessed in terms of their probabilities of being correct or incorrect. Such decisions include estimating the parameters of a statistical model, making predictions, and testing hypotheses. It is often possible to identify 'optimal' or at least good decisions, and Statistics is about these decisions, and knowing where they apply. A thorough grounding in Statistics, as provided by this course, is crucial not only for anyone contemplating a career in finance or industry, but also for scientists and policymakers, as we realise that some of the biggest issues, like climate change, natural hazards, or health, are also some of the most uncertain.

Relation to Other Units

This unit develops the Level 4 Probability and Statistics material, and is a prerequisite for some statistics units at Levels 6 and 7, namely Bayesian Modelling, Linear and Generalised Linear Models, and Theory of Inference.

Intended learning outcomes

Learning Objectives

By the end of the course the students should be able to:

  • Design powerful tests for statistical hypotheses, and understand both the power and the limitations of such tests.
  • Derive estimators of statistical parameters using Maximum Likelihood (ML), including assessment of their properties and measures of uncertainty.
  • Apply the Bayesian approach to estimation, prediction, and hypothesis testing, in the special case of conjugate analysis.
  • Use asymptotic arguments to understand the convergence of ML and Bayesian methods for large samples.
  • Choose appropriate statistical models for many common situations, and validate them.
  • Use the statistical computing enviroment R for routine statistical calculations, and plotting.

Transferable Skills

A clearer understanding of the logical constraints on inference; facility with the R environment for statistical computing.

Teaching details

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, computing exercises and/or other exercises
  • synchronous weekly group problem/example classes, workshops and/or tutorials
  • synchronous weekly group tutorials
  • synchronous weekly office hours

Assessment Details

80% Timed, open-book examination 20% Coursework

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.

If you fail this unit and are required to resit, reassessment is by a written examination in the August/September Resit and Supplementary exam period.

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. MATH20800).

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