Undergraduate Course: Statistical Case Studies (MATH10102)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Full year consultancy style projects course |
| Course description |
Students taking this course will work on two real-world statistical projects that are meant to simulate consultancy-style work in a business/research environment. Each project will be worked on by a small (2-3) team of students and will include complex real-world data and detailed background and problem statements. Students will be provided with several introductory lectures covering the data, questions, and review of relevant methods at the beginning of each semester and supported over the rest of the semester with weekly drop-in clinics. For each project, teams will produce a written report covering relevant background, details and implementation of methods used, results obtained and corresponding interpretation of the results. The students will also be asked to prepare a short talk and/or poster on their work which is be presented at the end of Semester 2.
|
Course Delivery Information
|
| Academic year 2024/25, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Full Year |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 6,
Supervised Practical/Workshop/Studio Hours 44,
Summative Assessment Hours 3,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
143 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Project 1 (Semester 1) 45%, Project 2 (Semester 2) 45%, Presentation / Poster(Semester 2) 10% |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Identify and apply appropriate¿statistical¿techniques to real application problems.
- Write a¿statistical¿consultancy style report.
- Skillfully present and defend completed work in an oral presentation or poster.
- Work in a team to complete an open ended consultancy project.
|
Reading List
Faraway, J.J., 2015.¿Linear models with R. CRC press. 2nd edition.
Faraway, J.J., 2016.¿Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC press.
James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013.¿An introduction to statistical learning¿(Vol. 112, p. 18). New York: Springer.
Wood, S.N., 2017.¿Generalized additive models: an introduction with R. CRC press. 2nd edition. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | SCS |
Contacts
| Course organiser | Dr Gordon Ross
Tel: (0131 6)50 51111
Email: Gordon.Ross@ed.ac.uk |
Course secretary | Miss Kirstie Paterson
Tel:
Email: Kirstie.Paterson@ed.ac.uk |
|
|
University Homepage