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. Andrieu |

Open unit status | Not open |

Pre-requisites |
MATH11300 Probability 1 and MATH 11400 Statistics 1 |

Co-requisites |
None |

School/department | School of Mathematics |

Faculty | Faculty of Science |

Unit aims

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

Unit homepage: http://www.maths.bris.ac.uk/~maxca/stats2/

General Description of the Unit

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 & Statistics material, and is a prerequisite for some statistics units at Levels 6 and 7, namely Bayesian Modelling A, Generalised Linear Models, and Theory of Inference, and desirable for Linear Models.

Further information is available on the School of Mathematics website: http://www.maths.bris.ac.uk/study/undergrad/

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.

Three lectures a week, and one problems class. Weekly homework, and weekly/fortnightly office hours for statistics and for computing.

80% Examination and 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.

The main text is:

- Rice, J. A. 1995 Mathematical statistics and data analysis, Duxbery Press, 2nd Ed.
*This is now out in a 3rd edition, either one will be fine, but references will be to the second edition.*

Also informative and useful:

- Morris H, DeGroot, and Mark J Schervish. 2002 Probability & Statistics, Addison Wesley, 3rd Ed.

Other reading will be given on the unit homepage (see Unit Aims).