Module manager: Dr Arief Gusnanto
Email: A.Gusnanto@leeds.ac.uk
Taught: Semester 1 Jul to 31 Aug View Timetable
Year running 2024/25
N/A
| OMAT5101M | Statistical Methods |
N/A
This module is not approved as an Elective
In big data with multiple variables, it is vital to discover pattern and infer valuable information from the data. This module introduces basic techniques from multivariate statistics, with the aim to discover, describe and exploit dependencies between variables in complex datasets.
The module will equip students with techniques to deal with multi-dimensional data using the techniques stated in the learning outcomes. Students will understand the theory through practice by engaging with example data sets.
On completion of the module, the student should be able to:
1. discover and exploit dependency between variables;
2. reduce the dimension of a dataset with dependent components, and interpret the results;
3. identify clusters in a given data set;
4. visualise similarities between observations in lower dimensions.
Skills developed in this module include:
- evaluating the quality of data and selecting suitable methods of analysis.
- interpreting data and making decisions based on that interpretation.
- Introduction to multivariate analysis
- Statistical dependence, covariance matrix
- High dimensional problems, the "curse of dimensionality"
- Principal Component Analysis (PCA), dimension reduction
- Clustering, K-means method, distances between/within clusters
- Multidimensional Scaling (MDS)
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 5 | 1 | 5 |
| Discussion forum | 6 | 2 | 12 |
| Seminar | 1 | 1.5 | 1.5 |
| Independent online learning hours | 42 | ||
| Private study hours | 89.5 | ||
| Total Contact hours | 18.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Students will have weekly formative assignments (e.g. quizzes, problem sheets or practical tasks) for each taught unit of the module and will be given model solutions with comments.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| In-course Assessment | Students will be tested predominantly using e-assessment methods or MCQs. | 20 |
| Assignment | The assignment will require students to complete a written report which may feature components of R code, R outputs, calculations and critical analysis of results. It is expected that the assignment will be completed in one week. | 80 |
| Total percentage (Assessment Coursework) | 100 | |
Students will resit by completing the Assignment (which covers all learning outcomes) six months after the delivery of the module.
There is no reading list for this module
Last updated: 4/29/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team