Applied Statistical Modelling
| Code | School | Level | Credits | Semesters |
| MATH3029 | Mathematical Sciences | 3 | 20 | Spring UK |
- Code
- MATH3029
- School
- Mathematical Sciences
- Level
- 3
- Credits
- 20
- Semesters
- Spring UK
Summary
This course extends the theoretical aspects of statistical inference (first met in MATH2011 or MATH4019) by developing the theory of the generalised linear model and its practical implementation. Initially, designing of experiments in order to explore relationship between factors and a response is viewed within the context of Linear models. The course then extends the understanding and application of statistical methodology established in previous courses to the analysis of discrete data and survival, which frequently occur in diverse applications. In the course students will be trained in the use of an appropriate high-level statistical package.
Target Students
Single Honours, Joint Honours Maths and Economics and MSc students from the School of Mathematical Sciences.
Co-requisites
Modules you must take in the same academic year, or have taken in a previous year, to enrol in this module:
Classes
- One 2-hour lecture each week for 12 weeks
- Two 1-hour lectures each week for 12 weeks
- One 1-hour computing each week for 12 weeks
Assessment
- 20% Coursework 1: Individual coursework. The coursework will consist of a mixture of theoretical questions and those involving the use of a statistical package.
- 80% Exam 1 (3-hour): Written exam.
Assessed by end of spring semester
Educational Aims
The purpose of this module is to broaden the students' knowledge and experience of applied statistics, including some aspects of medical statistics, by studying a wide range of statistical models and methods relevant to the modelling of datasets arising from diverse applications. This module is in the Statistics Pathway and builds upon the statistical ideas and methods introduced in the module G12SMM. Students will acquire knowledge and skills relevant to a professional statistician.Learning Outcomes
A student who completes this course successfully should be able to:
L1 State and prove standard results relating to the theory and methods of generalized linear models, used in various applications such as medical and financial statistics;
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.