ST425
Statistical Inference: Principles, Methods and Computation
This information is for the 2020/21 session.
Teacher responsible
Dr Wicher Bergsma COL.6.06
Availability
This course is compulsory on the MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (ÐÓ°ÉÂÛ̳ and Fudan), MSc in Statistics (Financial Statistics) (Research), MSc in Statistics (Research), MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available on the MRes/PhD in Management (Marketing) and MSc in Social Research Methods. This course is available with permission as an outside option to students on other programmes where regulations permit.
Pre-requisites
A knowledge of probability and statistics to the equivalent level of ST102 Elementary Statistical Theory.
Course content
The course provides a comprehensive coverage of fundamental aspects of methods and principles in probability and statistics, as well as linear regression analysis. Real data illustrations with the statistical package R forms an integral part of the course, providing a hands-on experience in simulation and data analysis.
Teaching
38 hours of lectures, 15 hours of seminars and 10 hours of computer workshops in the MT.
This course will be delivered through a combination of classes and lectures totalling a minimum of 60 hours across Michaelmas 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 does not include a reading week, instead Week 11 will be used as a revision week.
Formative coursework
Students will complete weekly assessed problem sheets. They will also complete R practice exercises following instructions from the weekly computing workshop.
Indicative reading
L. Wasserman, All of Statistics.
Y. Pawitan, In All Likelihood
K. Knight, Mathematical Statistics
A. Zuur et al., A Beginner's Guide to R. (Available online from ÐÓ°ÉÂÛ̳ Library.)
N. Venables et. al., An Introduction to R (http://cran.r-project.org/doc/manuals/R-intro.pdf)
Assessment
Exam (85%, duration: 3 hours) in the January exam period.
Project (15%) in the MT.
Student performance results
(2016/17 - 2018/19 combined)
| Classification | % of students |
|---|---|
| Distinction | 37.4 |
| Merit | 23.2 |
| Pass | 31 |
| Fail | 8.4 |
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: 52
Average class size 2019/20: 53
Controlled access 2019/20: No
Value: One Unit
Personal development skills
- Leadership
- Self-management
- Team working
- Problem solving
- Application of information skills
- Communication
- Application of numeracy skills
- Specialist skills
