Statistical Inference: Principles, Methods and Computation

This information is for the 2018/19 session.

Teacher responsible

Dr Wicher Bergsma COL.6.06


This course is compulsory on the MSc in Statistics, MSc in Statistics (Financial Statistics), 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.


A knowledge of probability and statistics to the equivalent level of ST102 Elementary Statistical Theory.

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.


38 hours of lectures, 4 hours and 30 minutes of lectures, 11 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. Pawitan, 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 (


Exam (85%, duration: 3 hours) in the January exam period.
Project (15%) in the MT.

Student performance results

(2014/15 - 2016/17 combined)

Classification % of students
Distinction 30.8
Merit 22.6
Pass 37.6
Fail 9

Key facts

Department: Statistics

Total students 2017/18: 52

Average class size 2017/18: 50

Controlled access 2017/18: No

Value: One Unit

Guidelines for interpreting course guide information

Personal development skills

  • Leadership
  • Self-management
  • Team working
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Specialist skills