Show/hide contentOpenClose All
Curricular information is subject to change
By the end of the course you should have enough grasp of R to complete a simple statistical project: tidying your data, graphing it and analysing it econometrically.
Indicative Module Content:1. Tidying data.
2. Graphing data.
3. Ordinary least squares.
4. Time series.
5. Other regression techniques.
6. Basic machine learning.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Autonomous Student Learning | 76 |
Total | 100 |
Not applicable to this module.
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
Not yet recorded.