Undergraduate Course: Applied Statistics (MATH10096)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 3 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course introduces a variety of advanced forms of regression models, going beyond the classical normal linear regression model. Emphasis will be placed on the analysis of real data and on how to draw meaningful conclusions from it. The R software will be extensively used.
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| Course description |
Topics to be covered may include:
- Review of linear models.
- Generalised regression models (binary, multi-categorical and count responses).
- Random effects models.
- Introduction to generalised additive models and smoothing.
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Information for Visiting Students
| Pre-requisites | Visiting students are advised to check that they have studied the material covered in the syllabus of any pre-requisite course listed above before enrolling. |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
69 )
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| Assessment (Further Info) |
Written Exam
80 %,
Coursework
20 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Coursework 20%, Examination 80% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S2 (April/May) | MATH10096 Applied Statistics | 00 | | | Resit Exam Diet (August) | Applied Statistics (MATH10096) | 00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a systematic understanding of advanced forms of regression models and be able to fit models to datasets where the use of the linear regression model is inappropriate.
- Conduct estimation and inference for model parameters, check model assumptions, and conduct model selection.
- Use R to analyse real datasets, interpret the corresponding results, and communicate them clearly and concisely.
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Reading List
Faraway, J.J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. 2nd edition. CRC Press.
Roback, P., and Legler, J. (2021). Beyond Multiple Regression: Applied Generalized Linear Models and Multilevels Models in R. CRC Press.
Gelman, A., Hill, J., and Vehtari, A. (2020). Regression and Other Stories. Cambridge University Press |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | ASta,Regression Models |
Contacts
| Course organiser | Dr Vanda Fernandes Inacio De Carvalho
Tel: (0131 6)50 4877
Email: |
Course secretary | Miss Kirstie Paterson
Tel:
Email: |
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