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Curricular information is subject to change
Upon course completion, students will be well-prepared to:
- Master fundamental quantitative methods, from basic analysis to advanced regression, effectively conveying numerical data.
- Evaluate published research critically, applying linear models skillfully.
- Translate concepts from class into programming skills in R and LaTeX, aligning with industry and academia norms.
The curriculum will cover these key areas:
• Exploring and manipulating data
• Linear regression analysis
• Logit and Probit models
• Model specification and diagnostics
• Dummy variable and interactions
Student Effort Type | Hours |
---|---|
Lectures | 15 |
Computer Aided Lab | 12 |
Autonomous Student Learning | 200 |
Total | 227 |
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.