ST211      Half Unit
Applied Regression

This information is for the 2025/26 session.

Course Convenor

Availability

Requisites

Mutually exclusive courses:

This course cannot be taken with ST201 at any time on the same degree programme.

Additional requisites:

Students who have no previous experience in R are required to 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)

Students must have completed one of the following two combinations of courses: (a) ST102, or (b) ST109 and EC1C1. Equivalent combinations may be accepted at the lecturer’s discretion.

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

30 hours of computer workshops and 20 hours of lectures in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

Regular Moodle quizzes.

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.

Project (10%, 1500 words) in Winter Term Week 6

This component of assessment includes an element of group work.

Project (35%, 1500 words) in Spring Term Week 2

Project (55%, 2500 words) in Spring Term Week 2

This component of assessment includes an element of group work.

10% project: A group work mini-project to be handed in at the end of reading week. This is followed by a 15 minute face to face feedback session which is also the main purpose of this assessment. This project would ideally be formative, however students do not do it if it is formative.

35% project: An individual logistic regression project to be handed in by the second week of the ST. For this component to help safeguard the integrity of the individual projects 10% of students will be subject to randomised interviews. The aim of these interviews is to check against the unauthorised use of GenAI and that the work submitted was the students’ own.

           55% project: A group multiple linear regression project to be handed in by the second week of the ST.