Module overview
Aims and Objectives
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- Fit multiple regression models using the adopted software package, including for models with qualitative variables and interactions
- Recall definitions of probability function, density function, cumulative distribution function and generating functions, and their inter-relationships
- Determine and interpret independence and conditional distributions
- Understand how to make a critical appraisal of a fitted model
- Understand the concept of maximum likelihood
- Use a variety of procedures for variable selection
Syllabus
Random variables revision
Inference basics revision
Joint/Bivariate distributions (marginal & conditionals, conditional expectation)
Multivariate normal distributions
Maximum likelihood
Linear regression (multiple with simple as a special case)
Least squares estimation (from ML perspective) using matrix notation
Diagnostics
Model selection
Qualitative explanatory variables
Interactions
Learning and Teaching
Teaching and learning methods
Lectures, workshops, computer labs
| Type | Hours |
|---|---|
| Teaching | 48 |
| Independent Study | 102 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Exam | 50% |
| Coursework | 50% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Exam | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Exam | 100% |