# Data Analytics for Government MSc/PgDip/PgCert: Online distance learning

## Predictive Modelling (ODL) STATS5076

• 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

### Short Description

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

### Timetable

The course mostly consists of asynchronous teaching material.

### Excluded Courses

Linear Models 3

Statistics 3L: Linear Models

Regression Models (Level M)

-/-

### 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.