- UCAS course code
- GG41
- UCAS institution code
- M20
Bachelor of Science (BSc)
BSc Computer Science and Mathematics with Industrial Experience
Duration: 4 years
Year of entry: 2025
UCAS course code: GG41 / Institution code: M20
- Key features:
Industrial experience
Scholarships available
- Typical A-level offer: A*A*A including specific subjects
- Typical contextual A-level offer: AAA including specific subjects
- Refugee/care-experienced offer: AAB including specific subjects
- Typical International Baccalaureate offer: 38 points overall with 7,7,6 at HL, including specific requirements
Fees and funding
Fees
Tuition fees for home students commencing their studies in September 2025 will be £9,535 per annum (subject to Parliamentary approval). Tuition fees for international students will be £36,000 per annum. For general information please see the undergraduate finance pages.
Policy on additional costs
All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).
Scholarships/sponsorships
The University of Manchester is committed to attracting and supporting the very best students. We have a focus on nurturing talent and ability and we want to make sure that you have the opportunity to study here, regardless of your financial circumstances.
For information about scholarships and bursaries please visit our undergraduate student finance pages .
Course unit details:
Linear Regression Models
| Unit code | MATH27711 |
|---|---|
| Credit rating | 10 |
| Unit level | Level 2 |
| Teaching period(s) | Semester 1 |
| Available as a free choice unit? | No |
Overview
In many areas of science, technology and medicine, researchers are often interested in two objectives: one is to explore the relationship between one observable random response and a number of explanatory variables; the other is to analyze the variability of the responses. Many statistical techniques investigate these objectives through the use of linear regression models. This course presents the theory and practice of these models.
Pre/co-requisites
| Unit title | Unit code | Requirement type | Description |
|---|---|---|---|
| Linear Algebra | MATH11022 | Pre-Requisite | Compulsory |
| Probability I | MATH11711 | Pre-Requisite | Compulsory |
| Statistics I | MATH11712 | Pre-Requisite | Compulsory |
Aims
The particular aims are to enable the students to:
1. Understand linear regression model with one or multiple independent variables.
2. Understand general linear model with continuous independent variables.
3. Understand classification models for one and two factors.
4. Understand ANCOVA models for one factor and multiple continuous independent variables.
Learning outcomes
- formulate, estimate and use regression linear models that are suitable for relevant statistical studies
- formulate statistical hypotheses in terms of the model parameters and test such hypotheses
- obtain confidence intervals for linear combinations of the model parameters
- obtain prediction intervals for linear combinations of future responses
- identify the impact of outliers on regression line
- use R to implement methods covered in the course
Teaching and learning methods
Teaching is composed of two hours of lectures per week and one tutorial class per fortnight. And one Examples class in the week there is no tutorial. Some lecture time will be delivered through pre-recorded videos posted online. Teaching materials will be uploaded to Blackboard for reference and review.
Assessment methods
| Method | Weight |
|---|---|
| Other | 20% |
| Written exam | 80% |
Written Exam - 80%
One mid-term online timed Blackboard test - 20%
Feedback methods
Generic feedback will be provided after marks are released
Recommended reading
1. Kutner, M. H., Nachtsheim, C. J., Neter, J. & Li, W. (2005). Applied Linear
Statistical Models. (5th edition). McGraw-Hill/Irwin: Boston.
2. Montgomery, D. C. & Peck, E. A. (1992). Introduction to Linear Regression
Analysis (5th edition). Wiley: New York.
3. Weisberg, S., (2013). Applied Linear Regression (4th edition). Wiley.
4. James H. Stapleton, (2009). Linear Statistical Models. (2nd edition) John Wiley & Sons
Study hours
| Scheduled activity hours | |
|---|---|
| Lectures | 22 |
| Practical classes & workshops | 6 |
| Tutorials | 6 |
| Independent study hours | |
|---|---|
| Independent study | 66 |
Teaching staff
| Staff member | Role |
|---|---|
| Wentao Li | Unit coordinator |