ST201 Half Unit
Statistical Models and Data Analysis
This information is for the 2025/26 session.
Course Convenor
Dr Yunxiao Chen
Availability
This course is available on the BSc in Accounting and Finance, Erasmus Reciprocal Programme of Study and Exchange Programme for Students from University of California, Berkeley. This course is freely available as an outside option to students on other programmes where regulations permit. It does not require permission. This course is freely available to General Course students. It does not require permission.
Also available to students who have studied statistics and mathematics to the level of ST107 Quantitative Methods or equivalent.
This course is not controlled access. If you request a place and meet the criteria you are likely to be given a place.
Requisites
Mutually exclusive courses:
This course cannot be taken with ST211 or DS202A or DS202W at any time on the same degree programme.
Pre-requisites:
Students must have completed ST107 before taking this course.
Additional requisites:
Previous programming experience is not required, but students who have no previous experience in R must complete an online pre-sessional R course from the Digital Skills Lab before the start of the course
Course content
A second course in statistics with an emphasis on data analysis with applications in the social sciences. Students will gain hands on experience using R-- a programming language and software environment for data analysis and visualisation. The course contains five topics, including (1) principles of statistical analysis, including data preparation, statistical models, regression and classification, inference, prediction, and bias-variance tradeoff, (2) multiple linear regression, including its assumptions, inference, data transformations, diagnostics, model selection, (3) regression tree method, (4) logistic regression, including odds ratios, likelihood, classification, and ROC curve, and (5) Bayes rule for classification and linear discriminant analysis.
Teaching
2 hours of lectures in the Spring Term.
20 hours of lectures and 16 hours of classes in the Winter Term.
This course has a reading week in Week 6 of Winter Term.
Students will be given their assessed project in week 9 which is due in Week 1 of Spring Term.
Formative assessment
Exercise questions in computer workshops
Indicative reading
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York, NY: Springer.
Fox, J. (2015). Applied regression analysis and generalized linear models. Thousand Oaks, CA: Sage Publications.
Assessment
Exam (80%), duration: 120 Minutes in the Spring exam period
Project (20%)
A quantitative research project (worth 20% of the marks) and an exam (worth 80% of the marks)
Key facts
Department: Statistics
Course Study Period: Winter and Spring Term
Unit value: Half unit
FHEQ Level: Level 5
CEFR Level: Null
Total students 2024/25: 23
Average class size 2024/25: 12
Capped 2024/25: NoCourse selection videos
Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.
Personal development skills
- Leadership
- Self-management
- Team working
- Problem solving
- Application of information skills
- Application of numeracy skills
- Specialist skills