ST425
Statistical Inference: Principles, Methods and Computation
This information is for the 2022/23 session.
Teacher responsible
Prof 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.
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 (https://moodle.lse.ac.uk/course/view.php?id=7745)
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
This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 60 hours across Michaelmas Term. This course does not include a reading week, instead Week 11 will be used as a revision week.
Formative coursework
A pre-sessional self-study R course taking about 10 hours needs to be completed by the start of the term. 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
(2018/19 - 2020/21 combined)
| Classification | % of students |
|---|---|
| Distinction | 42.9 |
| Merit | 30.4 |
| Pass | 16.1 |
| Fail | 10.6 |
Key facts
Department: Statistics
Total students 2021/22: 47
Average class size 2021/22: 40
Controlled access 2021/22: No
Value: One Unit
Course 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
- Communication
- Application of numeracy skills
- Specialist skills
