Advanced Predictive Models (ODL) STATS5073
- Academic Session: 2019-20
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Summer
- Available to Visiting Students: No
- Available to Erasmus Students: No
- Taught Wholly by Distance Learning: Yes
This course is concerned with models which can account for a non-normal distribution of the response and/or the fact that data is not independent, but correlated.
The course mostly consists of asynchronous teaching material.
Requirements of Entry
The course is only available to online-distance learning students on the PGCert/PGDip/MSc in Data Analytics and Data Analytics for Government.
Generalised Linear Models
Generalised Linear Models (Level M)
Statistics 3G: Generalised Linear Models
100% Continuous Assessment
This will typically be made up of a project report (30%), an oral assessment (20%) and five homework exercises, including online quizzes (50%). Full details are provided in the programme handbook.
The aims of this course are:
■ to provide an overview of different generalisations of linear regression models
■ to acquaint students with the theory of exponential families;
■ to introduce generalised linear models;
■ to introduce the concept of a time series and to present a range of approaches for representing trends and seasonality
■ to illustrate how temporal correlation can be incorporated into a regression model
■ to illustrate how random effects can be incorporated into a regression model
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ explain and derive key aspects of the theory of exponential families and generalised linear models.
■ make correct use of models with various link functions and link distributions such as models for discrete data;
■ determine whether a time series exhibits any evidence of a trend, seasonality or short-term correlation;
■ define the class of ARIMA probability models;
■ determine an appropriate model for a data set from the class of ARIMA models;
■ predict future values for a given time series;
■ make correct use of regression models assuming correlated residuals as well as models based on generalised estimation equations;
■ explain the notion of a random effect, why and when it is useful and, in particular, how it differs from a fixed effect;
■ make correct use of hierarchical models with random effects.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.