ST425
Statistical Inference: Principles, Methods and Computation
This information is for the 2016/17 session.
Teacher responsible
Prof Qiwei Yao COL 7.16
Availability
This course is compulsory on the MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) and MSc in Statistics (Research). This course is available on the MSc in Social Research Methods. This course is available with permission as an outside option to students on other programmes where regulations permit.
Course content
The course will provide a comprehensive coverage on some fundamental aspects of probability and statistics methods and principles. It also covers linear regression analysis. Data illustration using statistical package R constitutes an integral part throughout the course, therefore provides the hands-on experience in simulation and data analysis.
Teaching
40 hours of lectures, 10 hours of seminars and 10 hours of computer workshops in the MT.
Week 11 will be used as a revision week.
Formative coursework
Students will complete weekly assessed problem sheets. They will also complete R practice following instructions from the weekly computing workshop.
Indicative reading
L. Wasserman, All of Statistics.
Y. Pawita, In All Likelihood
K. Knight, Mathematical Statistics
A. Zuur et al., A Beginner's Guide to R. (Available online from LSE 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 LT week 0.
Project (15%) in the MT.
Student performance results
(2012/13 - 2014/15 combined)
Classification | % of students |
---|---|
Distinction | 45.7 |
Merit | 20.7 |
Pass | 22.1 |
Fail | 11.4 |
Key facts
Department: Statistics
Total students 2015/16: 33
Average class size 2015/16: 32
Controlled access 2015/16: 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