Dr Yining Chen

This course is compulsory on the MSc in Operations Research & Analytics. This course is available on the MSc in Data Science. This course is available with permission as an outside option to students on other programmes where regulations permit.

Course not available on MSc in Statistics nor on MSc in Statistics (Financial Statistics) nor on MSc in Statistics (Social Statistics).

Basic knowledge in calculus and linear algebra, as well as a first course in probability and statistics.

This course will provide an introduction to methods of statistics and data analysis. The statistical software R will constitute an integral part of the course, providing hands-on experience of data analysis. The syllabus will consist of:

Part I - Introduction

- Statistical Software: R

- Data exploration and visualisation

- Probability, random variables and distribution

Part II - Tools of statistical inference

- likelihood

- estimation

- hypothesis testing

Part III - Regression Methods

- linear regression

- logistic regression

Part IV - Basic time series analysis (topics as time permits)

20 hours of lectures and 10 hours of computer workshops in the MT.

Students will be expected to produce 5 exercises in the MT.

The bi-weekly exercises will enable students to learn how to implement statistical methods in R and provide preparation for the project. They will also ensure that students learn about the different methods of statistics and data analysis, as assessed later on the examination. 

All of Statistics, by Larry Wasserman, Springer.

Data Analysis and Graphics using R: an Example-based Appoach, by John Maindonald an John Braun, CambridgeUniversity Press.

Exam (70%, duration: 2 hours) in the summer exam period.
Project (30%) in the MT.