Applied statistical modelling (M348) will develop your general statistical modelling skills beyond those delivered by Analysing Data (M248). This module extends simple linear regression to model a wide variety of dataset types.
Book 1: Linear models
You’ll start with a revision of simple linear regression, combined with an introduction to the statistical software used, namely R. Initially, simple linear regression will be extended in two separate ways: firstly, by including more than one continuous explanatory variable, and secondly to deal with situations when the explanatory variable is categorical. You’ll then see how these two extensions can be combined to form regression models with any number of variables, continuous or categorical. You’ll then finish this book by putting the modelling techniques you’ve learned so far into practice by building a statistical model to predict success at the Olympics. In doing so, you’ll discover how fitting a model is only one part of using data to answer a question.
Book 2: Generalised linear models
All models you consider in Book 1 assume that the response variable is continuous and can be modelled, possibly after transformation, using a normal distribution. Although this is often sufficient for data analysis, it is not in some situations. So, in Book 2, you’ll consider how linear models can be extended to cope with such situations. The resulting models are known as generalised linear models.
You’ll see that it’s possible to have models where the response distribution is a binomial distribution instead of a normal distribution. You’ll then see that it’s possible to use other distributions, such as the Poisson or exponential distribution. Finally, in this book, you’ll see how a particular form of generalised linear model, the loglinear model, can explore relationships between categorical variables. The loglinear model is beneficial when contingency tables relate to data with three or more categorical variables.
Book 3: Applications
Having extended the range of ‘regression’ tools in your data analysis toolbox, Book 3 focuses on two specialist applications of statistics: econometrics and data science. You’ll study only one of these.
In the econometrics strand, you’ll see how the assumptions associated with linear models can be problematic when applied to economic data. For example, data may represent observations made over time, so they’re not independent. In this strand, you’ll see how econometricians deal with such problems.
The data science strand focuses on a couple of topics of particular interest to data scientists. Firstly, you’ll focus on finding clusters in data: groups of observations that are similar but different from observations in other groups. Next, you’ll learn suitable techniques for grouping the data when we don’t have examples of any groups, or even know how many groups there should be! You’ll then consider the challenges that ‘big data’ brings and discuss what can be done to address some of these challenges.
You’ll finish with a unit that integrates the content in the module and helps you prepare for the end-of-module assessment.
The ability to analyse and interpret data is central to many careers in, for example, government, health, business, finance and market research. The material in this module explores the fundamental statistical techniques required for analysing and interpreting data. Statistical software packages are important data analysis tools for practising statisticians: the use of one such statistical software package is integral to this module. Another vital skill practising statisticians require is communicating the results from their data analyses. You’ll develop this skill through statistical report writing.
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:
For this module, we may use an assessment verification process to meet the requirements of relevant accrediting, professional, statutory or regulatory bodies. As part of this process, you may be asked to attend a short post-assessment video discussion, lasting around 15 minutes. During the discussion, you’ll need to show a photo ID and talk through your answers to a small number of questions with a tutor or a member of the module team. The discussion is not graded and is only used to confirm that you completed the assessment 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.
M348 is a compulsory module in our:
M348 is an option module in our:
Applied statistical modelling (M348) starts once a year – in October.
It will next start in October 2026.
We expect it to start for the last time in October 2029.
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.
| Start | End | Register by | England fee |
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| 03 Oct 2026 | 30 Jun 2027 | 10 Sep 2026 | £2,044 |
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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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