Predictive Modelling (ODL) STATS5076

  • Academic Session: 2024-25
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Taught Wholly by Distance Learning: Yes
  • Collaborative Online International Learning: No

Short Description

This course introduces students to predictive models for regression and classification.

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

Linear Models 3

Statistics 3L: Linear Models

Regression Models (Level M)

Co-requisites

-/-

Assessment

100% Continuous Assessment

The continuous assessment will typically be made up of one class test, a report, and three homework exercises, including online quizzes. Full details are provided in the programme handbook.

Course Aims

The aims of this course are:

■ to introduce students to predictive modelling using multiple linear regression as a showcase;

■ to present some of the distributional theory underpinning the normal linear models and the associated methods for testing and interval estimation;

■ to explain how the design matrix of a linear model can be constructed to accommodate categorical covariates or, through basis expansions, non-linear effects;

■ to describe and contrast several common methods for model assessment as well as variable and model selection;

■ to show students how to implement these statistical methods using the R computer package.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ formulate normal linear models in vector-matrix notation and apply general results to derive ordinary least squares estimators in particular contexts;

■ construct a design matrix incorporating categorical covariates or covariates with a nonlinear effect;

■ derive, evaluate and interpret point and interval estimates of model parameters;

■ conduct and interpret hypothesis tests in the context of the Normal Linear Model;

■ derive, evaluate and interpret confidence and prediction intervals for the response at particular values of the explanatory variables;

■ assess the assumptions of a normal linear model using residual plots and diagnostics;

■ make use of and critique different methods for assessing the performance of a predictive model such R2 or AIC/BIC and use these for model or variable selection;

■ identify scenarios where data may be considered to be smooth functions and apply suitable data analysis techniques;

■ implement these statistical methods using the R computer package;

■ frame statistical conclusions clearly.

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.