- Department: Mathematics
- Module co-ordinator: Dr. Yue Zhao
- Credit value: 10 credits
- Credit level: M
- Academic year of delivery: 2021-22
- See module specification for other years: 2022-23
Occurrence | Teaching period |
---|---|
A | Autumn Term 2021-22 to Spring Term 2021-22 |
B | Spring Term 2021-22 to Summer Term 2021-22 |
This module provides essential skills for self-reliantly carrying out statistical data analyses of real data, from the thorough formulation of the question to be investigated up to the presentation of the analysis' results.
The participants of the module first receive a general introduction to statistical modelling and practical data analysis including an overview of a selected range of statistical methods, then learn how to implement these in the statistical software environment R, before they each carry out and present two statistical analysis projects based on real data sets. The latter constitutes the most significant part of the module. The first and smaller statistical data analysis is carried out individually at the end of the autumn term. The second and larger project is to be completed in groups of 3 or 4 participants at the end of the spring term. Each of the projects involves the theoretical analysis of the studied problem, the conception of the data analysis, its realization in R, summarizing the analysis in a written report, and the professional presentation of the analysis' results in front of the fellow participants. The participants' mastery of the module content will also be tested through three class tests scattered through the two terms.
Upon completion of this module, students should
General academic and graduate skills to be obtained:
Indicative module content:
Task | Length | % of module mark |
---|---|---|
Essay/coursework Class Tests: Statistical Modelling |
N/A | 45 |
Groupwork Group Coursework: Statistical Modelling |
N/A | 40 |
Oral presentation/seminar/exam Group Presentation: Statistical Modelling |
N/A | 15 |
None
The group project report cannot be repeated as it represents a joint effort of the group members, while the reassessment concerns only the individual students who do not achieve a pass mark for the whole module.
Task | Length | % of module mark |
---|---|---|
Essay/coursework Reassessment: Data Analysis Project |
N/A | 100 |
Current Department policy on feedback is available in the undergraduate student handbook. Coursework and examinations will be marked and returned in accordance with this policy
Fahrmeir L, Kneib T, Lang S and Marx B (2013). Regression: Models, Methods and Applications. Springer
Hastie T, Tibshirani R and Friedman J (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edition. Springer
Haerdle W and Simar L (2007). Applied Multivariate Statistical Analysis. 2nd edition. Springer
Everitt B and Hothorn T (2011). An Introduction to Applied Multivariate Analysis with R. Springer
Kleiber C and Zeileis A (2008). Applied Econometrics with R. Springer