Not running 2022/23.
| Module Code | STU34502 |
| Module Name | Applied Linear Statistical Methods II |
| ECTS Weighting [1] | 5 ECTS |
| Semester taught | Semester 2 |
| Module Coordinator/s | Alessio Benavoli |
Module Learning Outcomes
On successful completion of this module, students will be able to:
- Demonstrate ways in which the multivariate linear regression model can be generalised to non-linear and non-Gaussian cases;
- Define the generalised linear model and implement an analysis with specific examples of this model;
- Motivate the use of deviance as a measure of model fit and its use in estimating prediction error.
Module Content
The topics covered are:
- Recap of linear regression
- The exponential family of distributions
- The generalised linear model
- Specific examples: binomial, Poisson, logistic, Negative Binomial, Zero-Inflated
- Deviance
- Applications and examples
- R programming
Teaching and learning Methods
Lectures
Assessment Details
| Assessment Component | Brief Description | Learning Outcomes Addressed | % of total | Week set | Week Due |
| Continuous assessment | Project | LO1, LO2, LO3 | 30% | 8 | 11 |
| Examination | In-person exam (2 hours) | LO1, LO2, LO3 | 100% |
Reassessment Details
In-person exam (2 hours, 100%)
Contact Hours and Indicative Student Workload
| Contact Hours (scheduled hours per student over full module), broken down by: | 33 hours |
| Lecture | 33 |
| Independent study (outside scheduled contact hours), broken down by: | 83 hours |
| Preparation for classes and review of material (including preparation for examination, if applicable | 65 |
| completion of assessments (including examination, if applicable) | 18 |
| Total Hours | 0 hours |
Recommended Reading List
Dobson, A. J., and A. G. Barnett. 2008. An Introduction to Generalized Linear Models. CRC Press, Third Edition.
Myers, R. H., D. C. Montgomery, G. G. Vining, and T. J. Robinson. 2010. Generalized Linear Models with Applications in Engineering and the Sciences. Wiley, 2nd edition.
Pawitan, Yudi. 2001. In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford Science Publications.
Tanner, M. A. 1996. Tools for Statistical Inference- Methods for the Exploration of Posterior Distributions and Likelihood Functions. Springer, 3rd Edition.
Module Pre-requisites
Prerequisite modules: either (STU12501 and STU12502) or STU23501
Module Co-requisites
None