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Curricular information is subject to change
Upon course completion, students will be well-prepared to:
- basic understanding of working with R and RStudio
- being able to wrangle, summarise, describe, and visualise statistical data
- basic understanding of statistical inference
- basic understanding of executing and interpreting multiple regression
- preliminary understanding of logistic regression
The curriculum will cover these key areas:
- Accessing and visualising data
- Simple regression
- Descriptive statistics
- Multiple regression
- Sampling distribution & Central Limit Theorem
- Hypothesis tests & confidence intervals in regression
- Categorical independent variables
- Writing up regression results
- Interaction models
- Logistic regression
Student Effort Type | Hours |
---|---|
Lectures | 12 |
Computer Aided Lab | 12 |
Autonomous Student Learning | 200 |
Total | 224 |
Not applicable to this module.
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.