ST211      Half Unit
Applied Regression

This information is for the 2019/20 session.

Teacher responsible

Dr Sara Geneletti (Columbia House 5.07)


This course is compulsory on the BSc in Business Mathematics and Statistics and BSc in Mathematics, Statistics, and Business. This course is available as an outside option to students on other programmes where regulations permit. This course is not available to General Course students.

Specifically the course is available to Accounting and Finance students who have taken ST102.



Course content

Statistical data analysis in R covering the following topics: Simple and multiple linear regression, Model diagnostics, Detection of outliers, Multicollinearity, Introduction to GLMs



10 hours of lectures and 20 hours of computer workshops in the LT. 2 hours of lectures in the ST.

Students will be given their assessed project to start on in week 6 which is due in at the beginning of ST. 

Formative coursework

Regular Moodle quizzes. Regular take home exercises.

Indicative reading

1. Gelman and Hill, Data analysis Using Regression and Multilevel/Hierarchical models (CUP, 2007) First part.

2. Neter, J., Kutner, M., Nachtsheim, C. and Wasserman, W. Applied Linear Statistical Models, McGraw-Hill, Fourth Edition. (2004).

3. Abraham, B. Ledolter, J. Introduction to Regression Modelling, Thomson Brooks Cole. (2006).

4. S. Weisberg Applied Linear Regression, Wiley, 3rd edition. (2005)(intermediate).

5. Fox (2016) Applied Regression Analysis and Generalized Linear Models.



Exam (50%, duration: 2 hours) in the summer exam period.
Project (45%) in the ST.
Project (5%) in the LT.

There are two projects, a mini-project in the LT reading week and a longer project due at the beginning of the ST.

Student performance results

(2016/17 - 2018/19 combined)

Classification % of students
First 35.7
2:1 35.7
2:2 16.3
Third 7.1
Fail 5.1

Key facts

Department: Statistics

Total students 2018/19: 36

Average class size 2018/19: 18

Capped 2018/19: No

Value: Half Unit

Guidelines for interpreting course guide information

Personal development skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Specialist skills