## ST211Half UnitApplied Regression

This information is for the 2018/19 session.

Teacher responsible

Dr Sara Geneletti (Columbia House 5.07)

Availability

This course is available 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.

Pre-requisites

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

Teaching

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.

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.

Assessment

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

(2015/16 - 2017/18 combined)

Classification % of students
First 41.6
2:1 41.6
2:2 9
Third 4.5
Fail 3.4

Key facts

Department: Statistics

Total students 2017/18: 30

Average class size 2017/18: 15

Capped 2017/18: Yes (60)

Lecture capture used 2017/18: Yes (LT)

Value: Half Unit

Guidelines for interpreting course guide information

PDAM skills

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