Other OU statistics modules focus on hands-on practical applications of statistical techniques, data interpretation, and statistical analyses. This module complements these modules by providing the mathematical theory underlying the methods and concepts, including a treatment of classical and Bayesian statistics. A considerable amount of mathematics is sometimes required for this development.
This module is delivered online, using exercises, animations, audio, and video segments. You will also receive printed versions of the main units, extra exercises, and a handbook.
The module is divided into four blocks of study.
Block 1: Review and distribution theory
The first block comprises a review unit and units introducing distribution theory. The review is primarily of fundamental statistical ideas of the type taught in Analysing data (M248) (see Entry requirements for details); there is also a speedy reminder of important relevant methods in mathematics, including calculus and matrices. Two units in this block introduce the theory of continuous distributions. You will learn, for example, how to evaluate moments of distributions and about other properties of some important univariate distributions. The mathematical structure of multivariate distributions will be explored, with some emphasis on the multivariate normal distribution.
Block 2: Classical inference
The second block is about the classical approach to statistical inference. You will learn how to use calculus to obtain maximum likelihood estimators of parameters. You will also learn about the properties of maximum likelihood estimation and point estimation more generally. The mathematics underlying hypothesis tests and confidence intervals will be explored. There is also a unit on asymptotic (large sample) analysis, giving an insight into how statisticians study properties of statistical procedures by approximate methods.
Block 3: Bayesian statistics
In the third block, you’ll consider the Bayesian approach to statistical inference. The emphasis is first on so-called conjugate analysis, which constitutes the type of Bayesian analysis most amenable to straightforward mathematical development. You’ll consider prior to posterior analysis first, followed by Bayesian estimation based on decision theory. Markov chain Monte Carlo (MCMC) is a technique often used for tackling Bayesian problems which are not conjugate; you’ll investigate the mathematical ideas leading to the basic methods of MCMC.
Block 4: Linear modelling
The fourth and final block provides some of the mathematical development underlying linear modelling. The material covers linear regression on a single explanatory variable, multiple linear regression where there is more than one explanatory variable, and generalised linear modelling for regression situations where the normal distribution is not a suitable model for variation in the response. Both classical and Bayesian approaches to the analysis of these models are considered.
The full content list is on the Open mathematics and statistics website.
Successful study of this module should enhance your skills in understanding some useful mathematical theory, interpreting mathematical results in a statistical context, constructing logical arguments, and finding solutions to problems.
This module will provide you with the theoretical underpinning of some important statistical methods, giving you an enhanced understanding of, and the ability to modify and develop, the statistical toolbox used by professional statisticians in practice.
This module has been awarded a quality mark by the Royal Statistical Society, providing reassurance that the teaching, learning and assessment within this module is of high quality and meets the needs of students and employers.
You’ll get help and support from an assigned tutor throughout your module.
They’ll help by:
Online tutorials run throughout the module. While they’re not compulsory, we strongly encourage you to participate. Where possible, we’ll make recordings available.
Course work includes:
We’re using a new examination verification process for this module. We may ask you to attend a 15-minute post-exam video discussion, where you’ll present a photo ID and discuss your answers to a small number of questions with a tutor or member of the module team. The discussion isn’t graded; it’s only to verify that you completed the exam yourself.
You’ll have access to a module website, which includes:
We also provide physical:
You can study this module on its own or use the credits you gain towards an Open University qualification.
M347 is a compulsory module in our:
M347 is an option module in our:
Mathematical statistics (M347) starts once a year – in October.
It will next start in October 2026.
We expect it to start for the last time in October 2030.
As a student of The Open University, you should be aware of the content of the academic regulations, which are available on our Student Policies and Regulations website.
There is no formal prerequisite study, but you must have the required mathematical and statistical skills.
You can check you’re ready for M347 and see the topics it covers here.
You should aim to be confident and fluent with the concepts covered in the Are you ready? quiz here, and follow the advice in the quiz.
The key topics to revise include:
You’ll also find it useful to be familiar with the following topics:
An OU level 2 module in mathematics is ideal preparation, and Analysing data (M248) is also useful.
The OU strives to make all aspects of study accessible to everyone, and this Accessibility Statement outlines what studying M347 involves. You should use this information to inform your study preparations and any discussions with us about how we can meet your needs.
To find out more about what kind of support and adjustments might be available, contact us or visit our Disability support website.
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There may be extra costs on top of the tuition fee, such as set books, a computer and internet access.
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