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Accessibility statement
What do wage rates depend on? How many medals is a nation predicted to win at the next Olympics? Can we predict a student’s exam score based on their age and which qualification they’re studying? You can explore questions like these using statistical modelling techniques. This module takes a practical approach, emphasising the fitting of models and the interpretation of results. Also, depending on your interests, you’ll study topics related to econometrics (statistics applied to economics) or data science.
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.
There is no formal prerequisite study, but you must have the required mathematical and statistical skills.
Check you’re ready for M348 and see the topics it covers.
We recommend you have some experience using statistical software such as Minitab or SPSS. Analysing Data (M248) and an OU level 2 mathematics module will be ideal preparation.
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:
You’ll have access to a module website, which includes:
We also provide physical:
The OU strives to make all aspects of study accessible to everyone, and this Accessibility Statement outlines what studying M348 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.
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.
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