Computer-based statistical modelling

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• - Produce a technical report that describes problem formulation and solution
• Construct models and predictions that can be expected to have good statistical properties, based on exploring alternative models and using cross-validation.
• - Formulate statistical models and estimate their parameters. Interpret the estimated model.

• Revision of probability essentials
• Introduce maximum-likelihood estimation and develop it for selected parametric models such as independent sampling and regression, including generalized linear models. Develop confidence intervals and hypothesis tests via the asymptotic normality result
• Introduce optimal statistical decisions. Define a loss function and develop optimal binary classification as the main example.
• Selected topics in model selection. In-sample and out-of-sample error. Cross-validation for model selection.

Teaching and learning methods

A range of teaching and learning methods is used, centered on lectures, computer laboratories, and private study.

Resources & Reading list

Textbooks

Hastie, T and R. Tibshirani and J. Friedman (2009). The elements of statistical learning. Springer.

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External