Statistical Modelling of Discrete and Survival Data (Distance Learning)
| Code | School | Level | Credits | Semesters |
| MATH4080 | Mathematical Sciences | 4 | 20 | Spring UK |
- Code
- MATH4080
- School
- Mathematical Sciences
- Level
- 4
- Credits
- 20
- Semesters
- Spring UK
Summary
This course develops the theory of the generalised linear model and its practical implementation. The module builds upon and extends the Linear model introduced in the Foundations of Statistics module. The course extends the understanding and application of statistical methodology to the analysis of discrete (count and binary) data and survival models, which frequently occur in diverse applications. Students will gain experience of using statistical software to perform exploratory data analysis and to apply generalised linear model methodology to a wide range of applications. Students will develop key statistical skills in interpreting and communicating their statistical analysis.
Target Students
Available to MSc Statistics Science (Distance Learning) students.
Classes
- One 1-hour tutorial each week for 10 weeks
- One 1-hour computing each week for 10 weeks
This module is designed for distance learning programmes where delivery of material will largely be asynchronous through course notes support by lecture videos, Moodle quizzes and exercises. The tutorial will be used to support and reinforce the asynchronous learning. The computer software learning will be through specially designed workbooks with support sessions through online computer labs.
Assessment
- 50% Project: To assess ability to apply generalised linear models including using the statistical package R and to interpret and communicate statistical analysis. Individual project comprising a written report.
- 50% Exam 1 (2-hour): Written exam to assess statistical knowledge and understanding of statistical modelling approaches.
Assessed by end of spring semester
Educational Aims
The purpose of this module is to broaden the students’ knowledge and experience of applied statistics by studying a wide range of statistical models and methods relevant to the modelling of datasets arising from diverse applications. Real-life examples from medical, financial and environmental contexts will be used to highlight the wide applicability of the methods. Students will acquire knowledge and skills relevant to a professional statistician.Learning Outcomes
A student who completes this course successfully will be able to:
L1 - state and prove results relating to the theory and methods of generalised linear models;
L2 - apply the theory and methods to situations involving normal errors, binary data, count data and survival data;
L3 - derive point estimators and interval estimators and construct hypothesis tests, in the context of generalized linear models and survival analysis;
L4 - make effective use of statistical software in developing and implementing models for binary, count and survival data;
L5 - explain and interpret statistical results in the context of the relevant application giving rise to the data;
L6 - Write a report based on the analysis of a dataset.