Undergraduate Course: Statistical Case Studies (MATH10102)
|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
|Summary||Full year consultancy style projects course
Students will be assigned two consultancy style projects (one each semester) to work on in teams. Each project will take the form of a data analysis problem which 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 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 and the end of Semester 2.
Course Delivery Information
|Academic year 2020/21, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Project 1 (Semester 1) 45%, Project 2 (Semester 2) 45%, Presentation / Poster(Semester 2) 10%
|No Exam Information
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.
|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.
|Graduate Attributes and Skills
|Course organiser||Dr Nicole Augustin
|Course secretary||Mrs Alison Fairgrieve
Tel: (0131 6)50 5045