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Curricular information is subject to change
- basic understanding of working with R and RStudio
- being able to wrangle, summarise, describe, and visualise statistical data
- basic understanding of (frequentist) statistical inference
- basic understanding of executing and interpreting multiple regression
- preliminary understanding of logistic regression
Accessing and visualising data
Simple regression
Descriptive statistics
Multiple regression
Categorical independent variables
Writing up regression results
Interaction models
Sampling distribution & Central Limit Theorem
Hypothesis tests & confidence intervals in regression
Model specification and fit / statistical vs causal inference
Logistic regression
Student Effort Type | Hours |
---|---|
Lectures | 12 |
Computer Aided Lab | 12 |
Autonomous Student Learning | 200 |
Total | 224 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Homework assignment | Week 6 | n/a | Graded | No | 25 |
Continuous Assessment: Homework assignment | Week 3 | n/a | Graded | No | 25 |
Essay: Course paper | Coursework (End of Trimester) | n/a | Graded | No | 50 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
Feedback will be provided within 20 days from submission, as per university guidelines. Feedback on Homework 3 in particular will also count as formative assessment in preparation of the course paper.