Predictive Modelling (online) STATS5076
- Academic Session: 2018-19
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Available to Erasmus Students: No
- Taught Wholly by Distance Learning: Yes
This course introduces students to predictive models for regression and classification.
The course consists of short online lessons (each of at most 30 minutes length), totalling around 15-20 hours. Embedded in these lessons are formative quizzes and assessment tasks (not included in the above duration). These are flexible and can be taken (and re-taken) at any time. There also are 6-10 hours of tutorials and computer-based labs.
Requirements of Entry
The course is only available to students on the online MSc in Data Analytics.
Linear Models 3
Statistics 3L: Linear Models
Regression Models (Level M)
30% Continuous Assessment
70% Final exam (can be taken at test centres)
Main Assessment In: April/May
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 introduce students to logistic regression as an example of a discriminative method for classification;
■ to introduce students to linear discriminant analysis as an example of a generative method for classification;
■ 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;
■ contrast the discriminative approach to classification to the generative one;
■ explain the model used by and make use of logistic regression and linear discriminant analysis;
■ 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;
■ explain and interpret ROC curves and performance measures such as AOC
■ 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.