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Curricular information is subject to change
Learning OutcomesOn successful completion of the module students should be able to: • Understand the benefits and drawbacks of the scientific method and quantitative data approaches • Recognise and explain the appropriateness of statistical test for data analysis • Execute a number of common statistical tests in R • Interpret findings from these tests accurately • Report findings from quantitative data appropriately and accurately
Indicative Module Content:Indicative content (note this is subject to change)
Lecture 1- What is quantitative data?
Lecture 2- Experiment design and hypothesis development
Lecture 3- Concepts in stats: Means, Deviation and graphs
Lecture 4- Correlations: Positive or negative relationships
Lecture 5- Regression- the key to (most) statistics
Lecture 6- Multiple regression- modelling complexity
Lecture 7- T-test- The story of stout and stats
Lecture 8- ANOVA: comparing three groups
Lecture 9- Data analysis session/revision
Lecture 10- Online Quiz
Student Effort Type | Hours |
---|---|
Lectures | 12 |
Computer Aided Lab | 12 |
Specified Learning Activities | 30 |
Autonomous Student Learning | 71 |
Total | 125 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Class Test: Class Test | Week 10 | n/a | Standard conversion grade scale 40% | No | 30 |
Assignment: Dataset 1 Assignment | Week 6 | n/a | Standard conversion grade scale 40% | No | 35 |
Assignment: Dataset 2 Assignment | Week 12 | n/a | Standard conversion grade scale 40% | No | 35 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
• Online automated feedback
Dataset Assignment 1 & 2: Written feedback delivered via Brightspace post assessment Class Test: Online automated feedback given
Name | Role |
---|---|
Justin Edwards | Lecturer / Co-Lecturer |
Paola Peña | Lecturer / Co-Lecturer |
Yunhan Wu | Tutor |