ST201 Half Unit
Statistical Models and Data Analysis
This information is for the 2020/21 session.
Teacher responsible
Dr Yunxiao Chen COL 5.16
Availability
This course is available on the BSc in Accounting and Finance. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.
Also available to students who have studied statistics and mathematics to the level of MA107/ST107 Quantitative Methods or ST108 Statistical Methods for the Social Sciences or equivalent.
Pre-requisites
MA107/ST107 Quantitative Methods or ST108 Statistical Methods for the Social Sciences or equivalent.
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
This course will be delivered through a combination of classes and lectures totalling a minimum of 36 hours across Lent Term and 4 hours of lectures in the Summer Term. This year, some or all of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos. This course includes a reading week in Week 6 of Lent Term. Students will be given their assessed project to start on in week 6 which is due in at the end of LT.
Formative coursework
Moodle quizzes and a quantitative research project.
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: 2 hours) in the summer exam period.
Coursework (20%) in the LT.
Student performance results
(2017/18 - 2019/20 combined)
| Classification | % of students |
|---|---|
| First | 63.8 |
| 2:1 | 26.3 |
| 2:2 | 6.9 |
| Third | 2.1 |
| Fail | 0.9 |
Important information in response to COVID-19
Please note that during 2020/21 academic year some variation to teaching and learning activities may be required to respond to changes in public health advice and/or to account for the situation of students in attendance on campus and those studying online during the early part of the academic year. For assessment, this may involve changes to mode of delivery and/or the format or weighting of assessments. Changes will only be made if required and students will be notified about any changes to teaching or assessment plans at the earliest opportunity.
Key facts
Department: Statistics
Total students 2019/20: 100
Average class size 2019/20: 14
Capped 2019/20: No
Value: Half Unit
Personal development skills
- Leadership
- Self-management
- Team working
- Problem solving
- Application of information skills
- Application of numeracy skills
- Specialist skills
