Introducing your course
Find out what it's like to study MORSE at Lancaster University Management School.
10th for in Mathematics
The Guardian University Guide (2024)
11th for Research Quality for Economics
The Times and Sunday Times Good University Guide (2024)
14th for Mathematics
The Times and Sunday Times Good University Guide (2024)
BSc Mathematics, Operational Research, Statistics, and Economics (MORSE) (Industry) is a coherent degree designed for students who wish to apply their mathematical skills to solve real-world problems in business and industry.
Our programme covers:
The combination of these highly influential subjects will equip you with in-demand analytical, quantitative reasoning, statistical, optimisation, and programming skills that employers highly value, preparing you for careers like a business analyst, data scientist, operational researcher, or consultant, and opening doors to academic research opportunities.
The structured nature of the programme means that, starting from the second year, you take increasing ownership of your studies, selecting modules based on your evolving interests and strengths. This engaging program, coupled with our renowned excellence in research and teaching, ensures that you will benefit from high-quality instruction provided by academics who are leaders in their respective fields.
Year one: You will gain a strong foundation in four main subjects.
After the first year, a broad range of specialist optional modules will allow you to tailor your programme to align with your interests and steer you towards a specific career path.
Year two: You will further develop your knowledge in Algebra, Statistics, Economics, and Operational Research. Optional modules in Operational Research, Game Theory, and Operations Management will be available to choose from.
Year three: Spend your third year in a full-time placement, gaining valuable graduate-level experience and professional skills. This hands-on experience will give you a strong advantage in securing employment after your degree.
Year four: There is one mandatory module that further advances your knowledge of Statistics, alongside several optional modules on Advanced Statistical Modelling, Machine Learning, Data Mining, Forecasting, and Financial Mathematics.
The University will use all reasonable effort to support you in finding a suitable placement for your studies. While a placement role may not be available in a field or organisation directly related to your academic studies or career aspirations, all placement roles offer valuable experience of working at a graduate level and gaining professional skills.
If you are unsuccessful in securing a suitable placement for your third year, you will be able to transfer to the equivalent non-placement degree scheme and would continue with your studies at Lancaster, finishing your degree after your third year. The University offers a range of shorter placement and internship opportunities for which you would be welcome to apply.
A MORSE degree will prepare you for various rewarding and well-paying careers or to continue your further studies. We are committed to ensuring that our graduates are highly sought after for their analytical thinking and wide range of technical skills.
Here are just some of the roles that our graduates have progressed into upon graduating:
UK firms that have employed our recent graduates include:
Lancaster University Management School has an award-winning careers team to provide a dedicated careers and placement service, offering a range of innovative services for management school students. You can take part in:
Lancaster University is dedicated to ensuring you not only gain a highly reputable degree, you also graduate with relevant life and work-based skills. Visit our Careers section for full details.
A level AAA including A level Mathematics or Further Mathematics OR AAB including A level Mathematics and Further Mathematics
GCSE English Language grade C or 4
IELTS 6.5 overall with at least 5.5 in each component. For other English language qualifications we accept, please see our English language requirements web pages.
International Baccalaureate 35 points overall with 16 points from the best 3 Higher Level subjects including 6 in Mathematics HL (either analysis and approaches or applications and interpretations)
BTEC Considered alongside A level Mathematics grade A
We welcome applications from students with a range of alternative UK and international qualifications, including combinations of qualification. Further guidance on admission to the University, including other qualifications that we accept, frequently asked questions and information on applying, can be found on our general admissions web pages.
Contact Admissions Team + 44 (0) 1524 592028 or via email@example.com
Delivered in partnership with INTO Lancaster University, our one-year tailored foundation pathways are designed to improve your subject knowledge and English language skills to the level required by a range of Lancaster University degrees. Visit the INTO Lancaster University website for more details and a list of eligible degrees you can progress onto.
Lancaster University offers a range of programmes, some of which follow a structured study programme, and some which offer the chance for you to devise a more flexible programme to complement your main specialism.
Information contained on the website with respect to modules is correct at the time of publication, and the University will make every reasonable effort to offer modules as advertised. In some cases changes may be necessary and may result in some combinations being unavailable, for example as a result of student feedback, timetabling, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes and new research. Not all optional modules are available every year.
Students are provided with an understanding of functions, limits, and series, and knowledge of the basic techniques of differentiation and integration. Examples of functions and their graphs are presented, as are techniques for building new functions from old. Then the notion of a limit is considered along with the main tools of calculus and Taylor Series. Students will also learn how to add, multiply and divide polynomials, and will learn about rational functions and their partial fractions.
The exponential function is defined by means of a power series which is subsequently extended to the complex exponential function of an imaginary variable, so that students understand the connection between analysis, trigonometry and geometry. The trigonometric and hyperbolic functions are introduced in parallel with analogous power series so that students understand the role of functional identities. Such functional identities are later used to simplify integrals and to parametrise geometrical curves.
This full-year module provides the foundation for your future study in Economics. It is divided into three parts. The first part provides a thorough introduction to Microeconomics (including the theory of demand, costs and pricing under various forms of market structure, and welfare economics). The second part provides a thorough introduction to Macroeconomics (including national income analysis, monetary theory, business cycles, inflation, unemployment, and the great macroeconomic debates).
The third part of the module, taught in parallel with the first two parts, shows how the key Micro- and Macroeconomics ideas can help us understand the world around us. In this part, we will use economic experiments to answer various questions, such as whether economists are selfish. We will analyse whether a sugar tax is a good idea, automation and the minimum wage, the structure, conduct and performance of big technology firms, and use the skills we have learned to analyse inequality, Brexit, and Covid-19. We will also discuss the distinction between transitory inflation and stagflation, central banks’ changing objectives, cryptocurrencies and the financial markets, fiscal and monetary policy responses to the pandemic, the Great Depression and the Great Recession, quantitative easing, currency crises, and the Euro debt crisis. Economics A is taught in conjunction with modules (ECON103 or MATH100, depending on the degree) which provide the quantitative foundations for further study in Economics.
This course extends ideas of MATH101 from functions of a single real variable to functions of two real variables. The notions of differentiation and integration are extended from functions defined on a line to functions defined on the plane. Partial derivatives help us to understand surfaces, while repeated integrals enable us to calculate volumes.
In mathematical models, it is common to use functions of several variables. For example, the speed of an airliner can depend upon the air pressure and temperature, and the direction of the wind. To study functions of several variables, we introduce rates of change with respect to several quantities. We learn how to find maxima and minima. Applications include the method of least squares. Finally, we investigate various methods for solving differential equations of one variable.
Introducing the theory of matrices together with some basic applications, students will learn essential techniques such as arithmetic rules, row operations and computation of determinants by expansion about a row or a column.
The second part of the module covers a notable range of applications of matrices, such as solving systems of simultaneous linear equations, linear transformations, characteristic eigenvectors and eigenvalues.
The student will learn how to express a linear transformation of the real Euclidean space using a matrix, from which they will be able to determine whether it is singular or not and obtain its characteristic equation and eigenspaces.
During this Preparation for Placement module, you will learn about the competitive recruitment processes in the UK and the skills and expertise employers expect you to evidence; how to produce excellent CVs and cover letters; how to make an impact on application forms, what to expect at interviews and assessment centres.
You will get to hear from final year students about their placement experience and a chance for you to learn about the placement opportunities on offer from graduate employers. You will be offered the opportunity to experience a mock interview with a real employer and attend a mock assessment centre. You will be shown the range of resources and support we offer in LUMS Careers and how that will continue throughout the placement programme, in order to seek a suitable year in industry placement.
Students compete with others nationally to secure placements and we also offer exclusive opportunities with employers, however, we cannot guarantee that all students will progress on to a year in industry placement.
Probability theory is the study of chance phenomena, the concepts of which are fundamental to the study of statistics. This module will introduce students to some simple combinatorics, set theory and the axioms of probability.
Students will become aware of the different probability models used to characterise the outcomes of experiments that involve a chance or random component. The module covers ideas associated with the axioms of probability, conditional probability, independence, discrete random variables and their distributions, expectation and probability models.
To enable students to achieve a solid understanding of the broad role that statistical thinking plays in addressing scientific problems, the module begins with a brief overview of statistics in science and society. It then moves on to the selection of appropriate probability models to describe systematic and random variations of discrete and continuous real data sets. Students will learn to implement statistical techniques and to draw clear and informative conclusions.
The module will be supported by the statistical software package ‘R’, which forms the basis of weekly lab sessions. Students will develop a strategic understanding of statistics and the use of associated software, which will underpin the skills needed for all subsequent statistical modules of the degree.
The techniques of Management Science, based on mathematics, statistics, analytics and computing, can be extremely powerful in helping to solve organisational problems and are widely used in practice. This module explains the business situations in which such techniques apply and shows how to use the techniques and interpret the results to make better business decisions. Techniques are introduced through a mix of lectures, computer workshops and tutorials at which tutors can give extra help. The techniques introduced include decision analysis, simulation, queueing analysis, computer algorithms and linear programming. To support development, students are introduced to probability and probability distributions and gain familiarity with useful computer tools such as Excel and Python programming.
In this module, students work on challenging case studies based on real problems. These provide the opportunity to apply the concepts and techniques of problem solving, making recommendations and reporting results. There is a stress on practical examples of using the techniques. The module lays a foundation for learning more advanced techniques later in the degree, and emphasises not only how to apply techniques, but also when (and when not) to apply them.
This module covers the skills needed to improve business process by modelling and simulation.
Computer simulation methods are among the most commonly used approaches within operational research and management science. This module teaches you the skills required to apply simulation successfully to help improve the running of a business, and it shows how companies can find good solutions by predicting the effects of changes before implementing them.
Modern simulation packages are a valuable aid in building a simulation model, and this module uses the Witness simulation package, which is widely used commercially. However, without the proper approach, the results of a simulation project can be incorrect or misleading. This module looks at each task required in a simulation project. It emphasises the practical application of simulation, with a good understanding of how a simulation model works being an essential part of this.
Students will be provided with the foundational results and language of linear algebra, which they will be able to build upon in the second half of Year Two, and the more specialised Year Three modules. This module will give students the opportunity to study vector spaces, together with their structure-preserving maps and their relationship to matrices.
They will consider the effect of changing bases on the matrix representing one of these maps, and will examine how to choose bases so that this matrix is as simple as possible. Part of their study will also involve looking at the concepts of length and angle with regard to vector spaces.
The objective of the course is to train students to use macroeconomic models to understand real-world economic phenomena. The students will learn how to interpret macroeconomic data and understand the implications of economic policies. The course will put emphasis on major issues related to economic growth, the causes of economic fluctuations, and the effectiveness of economic policy. We will investigate the link between financial openness and economic growth, and we will explain why emerging countries experience capital outflows. We will study the impact of the exchange rate regime on the effectiveness of fiscal policy, we will rationalise the increase of current account deficits in Europe after the beginning of the nineties, and we will analyse the cause(s) of cross-country differences in hours worked.
The module requires basic knowledge of basic calculus, logical thinking and problem-solving skills.
Various topics of interest to prospective managers are covered within this module, including production and demand, competition and strategic behaviour, advertising and distribution, capital budgeting and inventories, the foreign exchange market, the economics of the multinational enterprise and the politics of corporate economics. The module provides knowledge of aspects of microeconomics relevant to general management, and also emphasises techniques and tools of analysis alongside relevant theory.
The module is designed to as an introduction to aspects of the firm and its environment which are of particular relevance to management. The topics selected aim to bridge the gap between the traditional approach to managerial economics and the more modern study of the organisation.
This module describes a variety of optimisation algorithms and how business problems can be modelled using these techniques.
Optimisation is one of the primary techniques associated with management science/operational research. Sometimes called Mathematical Programming, optimisation is concerned with finding the ‘best’ solution to a problem that has a large number of possible solutions. It has a huge array of applications in many fields, and optimisation models are now used routinely in industry (especially in manufacturing, energy production and transport), in the public sector (especially defence and healthcare) and in the services (especially finance). Therefore, skills in formulating and solving optimisation problems are valuable for a variety of careers. The course is designed to enable students to apply optimisation techniques to business problems. Students should take it if they are interested in modelling real situations via mathematics. However, the goal is not mathematics for its own sake. We also want the students to have an understanding of the types of situations in which the various techniques can (or cannot) be applied.
Four main topics are covered:
Specially-structured linear programs
Integer and mixed-integer programming
Heuristics for large-scale problems
Probability provides the theoretical basis for statistics and is of interest in its own right.
Basic concepts from the first year probability module will be revisited and extended to these to encompass continuous random variables, with students investigating several important continuous probability distributions. Commonly used distributions are introduced and key properties proved, and examples from a variety of applications will be used to illustrate theoretical ideas.
Students will then focus on transformations of random variables and groups of two or more random variables, leading to two theoretical results about the behaviour of averages of large numbers of random variables which have important practical consequences in statistics.
Statistics is the science of understanding patterns of population behaviour from data. In the module, this topic will be approached by specifying a statistical model for the data. Statistical models usually include a number of unknown parameters, which need to be estimated.
The focus will be on likelihood-based parameter estimation to demonstrate how statistical models can be used to draw conclusions from observations and experimental data, and linear regression techniques within the statistical modelling framework will also be considered.
Students will come to recognise the role, and limitations, of the linear model for understanding, exploring and making inferences concerning the relationships between variables and making predictions.
In this first of three work-based learning modules, you will begin your reflective learning process. During the workshops, you will be tasked with creating reflective tools to support the development of your assignment. You will be assessed on a short written reflection that enables you to prepare for your continued professional development during your industrial placement.
This module introduces you to various current techniques for forecasting future customer demand, including a range of predictive models that develop your knowledge of the best ways of forecasting in problem situations.
The aim is to ensure that you have the skills needed to develop a validated quantitative set of forecasts using both extrapolative and causal forecasting methods, and that you can apply a simple forecasting method to support demand and revenue management.
You will also learn to identify and exploit opportunities for revenue optimisation in different business contexts. You review the main methodologies used in each of these areas, discuss legal issues associated with different pricing strategies, and survey current practices in different industries. Most of the topics covered are either directly or indirectly related to pricing issues faced by firms operating in environments where they enjoy some degree of market power.
This module provides an introduction to the use and impact of information, communication and integrated technology systems on business and organisations. It focusses not on technical specifications, but rather on managerial and business implications of using these systems.
The following issues will be addressed:
The course provides the business foundation for other more specialised or technical topics in Information Systems.
This module helps you improve your strategic thinking. Over the course of this module, you will learn how to use ‘games’ to model strategic situations in the real world, and how to analyse and find out solutions to these games in situations in which players are intelligent and rational. Games including “normal form games”, “extensive form games”, “Bayesian games”, “repetitive games”, and “games with correlation device” will be introduced. Opportunities for playing games with the lecturer and other students will also be provided. The module requires a basic knowledge of algebra, calculus, and economics.
Operations management is the core managerial discipline in all kinds of operation – from private-sector manufacturing through to public-sector services. It is about the human capacity to organise all the operations that underpin the modern world: transportation, the generation of energy, retailing, the production of goods, the provision of medical and educational services, and so on.
The module will introduce students to key concepts and themes of Operations Management such as operations strategy and performance objectives, operations design (e.g. layout, facility location and capacity), inventory planning and control, project management, quality management and supply chain management. These topics will be approached using a combination of qualitative and simple quantitative methods.
By the end of the course students should be able to:
Designed as an introduction to the theory and practice of managing business projects, this module introduces project management methods in a way which links to the life cycle of a typical project – from the early project identification and definition stages, through project execution and control, to issues of implementation and change. The coverage of the early stages of the project cycle uses methods emerging from the systems movement and stresses the strategic relevance of project management. The management of the project is covered by introducing techniques for planning, scheduling and controlling projects. Attention is also given to the people management aspects of this process, including leadership, team-working and motivation.
Many organisational recruiters have identified the skills and knowledge they want to see from a prospective employee. Some of the top priorities are spreadsheet modelling, problem structuring, statistics, and project management.
Students will be introduced to Microsoft Excel and the basics of dynamic model building, including skills such as data handling, filtering and analysis, using functions, and charting, plus advanced techniques such as optimisation, simulation, and the use of Visual Basic for Applications (VBA) to automate models and construct decision support models.
The course will make extensive use of case studies and workshop-orientated learning tasks.
This module examines the principles and practices of supply chain management, building on operations management concepts. It examines supply chain and logistics management applications in various sectors, such as retailing, pharmaceuticals, information technology, and even higher education.
Most of the time will be spent considering inter-organisational relationships from various perspectives, but it will also be necessary to understand how they relate to matters within the organisation, including functional areas such as logistics and procurement. As well as covering core principles and practices, the module also considers emerging supply chain themes such as service supply chains and sustainability.
This module supports you with your professional development whilst you are away on placement. You will consciously consider your learning opportunities and actively engage in reflective practice.
Statistical inference is the theory of the extraction of information about the unknown parameters of an underlying probability distribution from observed data. Consequently, statistical inference underpins all practical statistical applications.
This module reinforces the likelihood approach taken in second year Statistics for single parameter statistical models, and extends this to problems where the probability for the data depends on more than one unknown parameter.
Students will also consider the issue of model choice: in situations where there are multiple models under consideration for the same data, how do we make a justified choice of which model is the 'best'?
The approach taken in this course is just one approach to statistical inference: a contrasting approach is covered in the Bayesian Inference module.
On return from placement for the final year of your degree, you will engage in group discussions to reflect on your work-based learning experience and draw out relationships between theory and practice. You will complete a Portfolio of Professional Practice to showcase your developed skills and abilities, and write a critically reflective essay on your placement experience to further the connections made between theory and practice.
Many organisational recruiters have identified particular skills and knowledge they want to see from a prospective employee. Top in the priorities are spreadsheet modelling, problem structuring, statistics, and project management. This highly practical module equips you with an advanced set of spreadsheet modelling skills, including advanced functions and programming using Visual Basic for Applications (VBA) that can be used to produce a wide range of effective decision-support models.
You will learn general concepts about spreadsheet modelling using VBA and a wide range of modelling skills which are highly relevant to management. These include structured programming, program documentation, program verification, user interface design, and general investigative modelling – including applications involving optimisation, forecasting and simulation.
Bayesian statistics provides a mechanism for making decisions in the presence of uncertainty. Using Bayes’ theorem, knowledge or rational beliefs are updated as fresh observations are collected. The purpose of the data collection exercise is expressed through a utility function, which is specific to the client or user. It defines what is to be gained or lost through taking particular actions in the current environment. Actions are continually made or not made depending on the expectation of this utility function at any point in time.
Bayesians admit probability as the sole measure of uncertainty. Thus Bayesian reasoning is based on a firm axiomatic system. In addition, since most people have an intuitive notion about probability, Bayesian analysis is readily communicated.
At the heart of many real-world industrial and scientific problems are increasingly large data sets that need to be analysed efficiently in order to gain novel and useful insights. The field of as data mining (also known as intelligent data analysis) brings together real large-scale datasets and algorithms from statistics, machine learning and computational intelligence that can work efficiently with real-world datasets.
he course provides an introduction to the fundamental methods and approaches from the interrelated areas of data mining, statistical/ machine learning, and intelligent data analysis. The course covers the entire data analysis process, starting from the formulation of a project objective, developing an understanding of the available data and other resources, up to the point of statistical modelling and performance assessment. The focus of the course is classification. The course content covers:
-Exploratory data analysis including visualisation and simple feature selection methods
-Classification methods like: Logistic Regression, Decision trees (Random forests), k-Nearest Neighbours, and Naive Bayes
-Performance assessment and model selection
The course uses the R programming language and more specifically the RStudio integrated programming environment.
This course focuses on the economics of growth and development, both from a theoretical and empirical perspective. Using examples from developing countries, it explores wide-ranging, policy-relevant topics such as investments in health, education and infrastructure, microeconomics of credit markets, corruption and other determinants of economic development.
This applied module is an introduction to the economics of health and health care and will develop your awareness of the main policy issues in this field. It provides a comprehensive set of economic tools for critically appraising fundamental issues in the economics of health while offering a broad overview of the UK’s National Health Service and other health care systems around the world. The emphasis is on the use and interpretation of microeconomic models and the latest empirical evidence.
This module builds on basic microeconomics concepts to explore competition between firms and the evolution of market structure. It focuses on understanding the way firms make decisions and the effects of those decisions on market outcomes like prices, quantities, the type of products offered, and social welfare. The module first introduces basic concepts in Industrial Organisation to study imperfect competition and the determinants of market power. It then proceeds to analyse important topics in competition policy, such as cartels and merger policy.
The module requires an understanding of intermediate microeconomics (especially production/cost theory), basic concepts of game theory, and basic calculus.
This course provides an introduction to the economics of International Business and their key role in the global economy, incorporating the analysis of internationalising firms and multinational enterprises (MNE). Its conceptual foundations derive from the critical application of relevant economic theories and addresses policy issues highlighting the economic, strategic and financial challenges facing businesses that operate across international borders in a multiple exchange rate environment.
The course covers a range of important topics that facilitate a deeper understanding of the determinants and implications of the activities of international firms in the global economy. These topics include: global trends in research and development (R&D) and implications; the Global Factory, global value chains and the decline of the global firm; the growth and employment effects of international business; and exchange rates, risk and international financial management.
This module develops your understanding of concepts and theories of international trade and factor flows, with particular reference to the way in which such material can inform policymaking. Topics covered include the Ricardian model, the Heckscher-Ohlin model, international trade under imperfect competition, outsourcing and offshoring, trade models based on heterogeneous firms and multinational firms, and trade policy under perfect and imperfect competition. Throughout the module we emphasise the applicability of the models learned, and their relevance to real-world events. Examples include the relationship between labour productivity and wages, opinions toward free trade, and the impact of immigration.
Focusing on the microeconomics of labour and personnel, this module covers topics such as the economics of migration, wage determination, job search and labour market discrimination.
There is a particular emphasis on principal agent problems in human resources and the design of incentives within firms.
Economics theory is used to analyse the operation of labour markers and assess the empirical evidence. Areas covered include:
Using the classical problem of data classification as a running example, this module covers mathematical representation and visualisation of multivariate data; dimensionality reduction; linear discriminant analysis; and Support Vector Machines. While studying these theoretical aspects, students will also gain experience of applying them using R.
An appreciation for multivariate statistical analysis will be developed during the module, as will an ability to represent and visualise high-dimensional data. Students will also gain the ability to evaluate larger statistical models, apply statistical computer packages to analyse large data sets, and extract and evaluate meaning from data.
This module formally introduces students to the discipline of financial mathematics, providing them with an understanding of some of the maths that is used in the financial and business sectors.
Students will begin to encounter financial terminology and will study both European and American option pricing. The module will cover these in relation to discrete and continuous financial models, which include binomial, finite market and Black-Scholes models.
Students will also explore mathematical topics, some of which may be familiar, specifically in relation to finance. These include:
Throughout the module, students will learn key financial maths skills, such as constructing binomial tree models; determining associated risk-neutral probability; performing calculations with the Black-Scholes formula; and proving various steps in the derivation of the Black-Scholes formula. They will also be able to describe basic concepts of investment strategy analysis, and perform price calculations for stocks with and without dividend payments.
In addition, to these subject specific skills and knowledge, students will gain an appreciation for how mathematics can be used to model the real-world; improve their written and oral communication skills; and develop their critical thinking.
The aim is to introduce students to the study designs and statistical methods commonly used in health investigations, such as measuring disease, causality and confounding.
Students will develop a firm understanding of the key analytical methods and procedures used in studies of disease aetiology, appreciate the effect of censoring in the statistical analyses, and use appropriate statistical techniques for time to event data.
They will look at both observational and experimental designs and consider various health outcomes, studying a number of published articles to gain an understanding of the problems they are investigating as well as the mathematical and statistical concepts underpinning inference.
Policymakers at Central Banks lie in a unique position to influence economic activity. This module examines the role of monetary policy in influencing the expectations and behaviour of agents in the economy and the implications this has for outcomes such as inflation, GDP and household welfare. Students will focus on applications of monetary theory to central banks problems and the recent objectives of the Bank of England Monetary Policy Committee. Topics include Central Bank independence, inflation targeting and the zero lower bound on interest rates, money creation and quantitative easing, and the macroeconomics of pandemics.
Central to this module is the Crossbay Contracting Game, a management game designed by the module convenor and his colleagues at HCS Ltd.
Three (health service) organisations are involved in a contract negotiation, and you will be part of the management team of one of these organisations. The contract concerns funding requirements for core activities over the coming financial year.
The main aim is to reach an agreement that is satisfactory to all three parties – but you must of course ensure that your own organisation is likely to come out of it well. Much of your time will be spent analysing the emerging situation and negotiating with the other parties.
Alongside this 'management' task there is also a modelling task. Teams are provided with a decision support system they can use to analyse the emerging situation and help them decide which strategies are cost-effective for their organisation.
This module presents an overview of the interactions between the government, firms, and citizens, using a mix of theory and empirical work. Sometimes, markets are not efficient, and government intervention is necessary. Sometimes, markets are efficient, but equity concerns create the need for government. There is often a tension between the socially optimal policy and the outcome of the democratic process.
Some questions we study in this module:
The concept of generalised linear models (GLMs), which have a range of applications in the biomedical, natural and social sciences, and can be used to relate a response variable to one or more explanatory variables, will be explored. The response variable may be classified as quantitative (continuous or discrete, i.e. countable) or categorical (two categories, i.e. binary, or more than categories, i.e. ordinal or nominal).
Students will come to understand the effect of censoring in the statistical analyses and will use appropriate statistical techniques for lifetime data. They will also become familiar with the programme R, which they will have the opportunity to use in weekly workshops.
Important examples of stochastic processes, and how these processes can be analysed, will be the focus of this module.
As an introduction to stochastic processes, students will look at the random walk process. Historically this is an important process, and was initially motivated as a model for how the wealth of a gambler varies over time (initial analyses focused on whether there are betting strategies for a gambler that would ensure they won).
The focus will then be on the most important class of stochastic processes, Markov processes (of which the random walk is a simple example). Students will discover how to analyse Markov processes, and how they are used to model queues and populations.
Issues and problems in the complex world of management do not necessarily arise in a well-structured form. People often do not know what they want or what is possible. They may also disagree about what they are trying to achieve and the means for arriving at their goals. Much thinking needs to be done in order to define an appropriate framework within which a useful analysis or project can be carried out.
Various approaches have been developed in recent years to assist in this task, often referred to as problem-structuring methods (PSMs). These very practically oriented methodologies typically involve the management team to help facilitate the structuring of complex situations. They place emphasis on dialogue to think through strategic problems, identify the salient issues, formulate goals and negotiate action plans. This module introduces you to several PSMs and some of the process skills needed to use them.
Modern statistics is characterised by computer-intensive methods for data analysis and development of new theory for their justification. In this module students will become familiar with topics from classical statistics as well as some from emerging areas.
Time series data will be explored through a wide variety of sequences of observations arising in environmental, economic, engineering and scientific contexts. Time series and volatility modelling will also be studied, and the techniques for the analysis of such data will be discussed, with emphasis on financial application.
Another area the module will focus on is some of the techniques developed for the analysis of multivariates, such as principal components analysis and cluster analysis.
Lastly,students will spend time looking at Change-Point Methods, which include traditional as well as some recently developed techniques for the detection of change in trend and variance.
We set our fees on an annual basis and the 2025/26 entry fees have not yet been set.
As a guide, our fees in 2024/25 were:
There may be extra costs related to your course for items such as books, stationery, printing, photocopying, binding and general subsistence on trips and visits. Following graduation, you may need to pay a subscription to a professional body for some chosen careers.
Specific additional costs for studying at Lancaster are listed below.
Lancaster is proud to be one of only a handful of UK universities to have a collegiate system. Every student belongs to a college, and all students pay a small college membership fee which supports the running of college events and activities. Students on some distance-learning courses are not liable to pay a college fee.
For students starting in 2023 and 2024, the fee is £40 for undergraduates and research students and £15 for students on one-year courses. Fees for students starting in 2025 have not yet been set.
To support your studies, you will also require access to a computer, along with reliable internet access. You will be able to access a range of software and services from a Windows, Mac, Chromebook or Linux device. For certain degree programmes, you may need a specific device, or we may provide you with a laptop and appropriate software - details of which will be available on relevant programme pages. A dedicated IT support helpdesk is available in the event of any problems.
The University provides limited financial support to assist students who do not have the required IT equipment or broadband support in place.
In addition to travel and accommodation costs, while you are studying abroad, you will need to have a passport and, depending on the country, there may be other costs such as travel documents (e.g. VISA or work permit) and any tests and vaccines that are required at the time of travel. Some countries may require proof of funds.
In addition to possible commuting costs during your placement, you may need to buy clothing that is suitable for your workplace and you may have accommodation costs. Depending on the employer and your job, you may have other costs such as copies of personal documents required by your employer for example.
The fee that you pay will depend on whether you are considered to be a home or international student. Read more about how we assign your fee status.
Fees are set by the UK Government annually, and subsequent years' fees may be subject to increases. Read more about fees in subsequent years.
We will charge tuition fees to Home undergraduate students on full-year study abroad/work placements in line with the maximum amounts permitted by the Department for Education. The current maximum levels are:
International students on full-year study abroad/work placements will be charged the same percentages as the standard International fee.
Please note that the maximum levels chargeable in future years may be subject to changes in Government policy.
Details of our scholarships and bursaries for students starting in 2025 are not yet available. You can use our scholarships for 2024-entry applicants as guidance.
The information on this site relates primarily to 2025/2026 entry to the University and every effort has been taken to ensure the information is correct at the time of publication.
The University will use all reasonable effort to deliver the courses as described, but the University reserves the right to make changes to advertised courses. In exceptional circumstances that are beyond the University’s reasonable control (Force Majeure Events), we may need to amend the programmes and provision advertised. In this event, the University will take reasonable steps to minimise the disruption to your studies. If a course is withdrawn or if there are any fundamental changes to your course, we will give you reasonable notice and you will be entitled to request that you are considered for an alternative course or withdraw your application. You are advised to revisit our website for up-to-date course information before you submit your application.
More information on limits to the University’s liability can be found in our legal information.
We believe in the importance of a strong and productive partnership between our students and staff. In order to ensure your time at Lancaster is a positive experience we have worked with the Students’ Union to articulate this relationship and the standards to which the University and its students aspire. View our Charter and other policies.
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