Unit information: Theory of Inference in 2013/14

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Unit name Theory of Inference
Unit code MATH35600
Credit points 20
Level of study H/6
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Jonty Rougier
Open unit status Not open
Pre-requisites

MATH11300, MATH11400, Statistics 2 would be useful but is not a prerequisite

Co-requisites

None

School/department School of Mathematics
Faculty Faculty of Science

Description including Unit Aims

Aims The basic premise of inference is our judgment that the things we would like to know are related to other things that we can measure. This premise holds over the whole of the sciences. The distinguishing features of statistical science are:

  • A probabilistic approach to quantifying uncertainty, and, within that,
  • A concern to assess the principles under which we make good inferences, and
  • The development of tools to facilitate the making of such inferences.

In a selective approach, this course considers aspects of evidence, of experimental design, and some of the basic principles of inference. The intention is always to illuminate current practice, eg as found in the courtroom, in medical trials, or more widely in designed (and ad hoc) experiments.

Intended Learning Outcomes

To gain an understanding of some key principles of statistical inference, and how these impact upon current practice across a range of fields.

Transferable Skills: This unit exemplifies the general skills of other mathematical units, of logical thinking and the concept of proof, problem solving, abstraction, a facility with notation, self-study and self-appraisal. Some examples and homeworks will use the statistical computing environment R.

Teaching Information

Lectures, problems classes, homeworks to be done by students, Office Hours.

Assessment Information

100% examination

Reading and References

There is no set book for the unit. The following textbooks will cover all of the basic material, with a careful treatment of the more subtle issues that often confound non-statisticians. These are listed in increasing order of sophistication: 1.David Freedman et al, Statistics, Norton, 4th edn (earlier editions also good), 2007 2.John Rice, Mathematical Statistics and Data Analysis, Duxbury Press, 2nd edn, 1995. 3.Morris DeGroot and Mark Schervish, Probability and Statistics, Addison Wesley, 3rd edn, 2002.

In addition, the following books are highly recommended: 1.Stephen Senn, Dicing with death: Chance, risk, and health, CUP, 2003. 2.Gerd Gigerenzer, Reckoning with risk: Learning to live with uncertainty, Penguin, 2003. 3.Imogen Evans et al, Testing treatments: Better research for better healthcare, Pinter & Martin Ltd., 2nd edition, 2011

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