Advanced Predictive Models (ODL) STATS5073

  • Academic Session: 2018-19
  • 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 students on the online PGCert/PGDip/MSc in Data Analytics.

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.