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

Short Description

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.

Timetable

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.

Excluded Courses

Generalised Linear Models

Generalised Linear Models (Level M)

Statistics 3G: Generalised Linear Models

Co-requisites

-/-

Assessment

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.

Course Aims

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.