Flexible Regression

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Understand the concept of random effects, and their role in modelling common forms of grouped data.
• Understand and apply basic criteria that define successful statistical modelling.
• Apply common nonparametric modelling methods to data with both single and multiple predictor variables.
• Understand the theoretical underpinnings of the discussed modelling methods, and their implementation in appropriate statistical software.

• Fundamentals of random effect modelling and population versus subject-specific models.
• Linear mixed models for continuous data, generalised least squares and restricted maximum likelihood.
• Generalised linear mixed models for discrete data, likelihood estimation and numerical methods.
• Common univariate smoothing methods including kernel smoothing and smoothing splines.
• Generalised additive models.
• Cross-validation and bias-variance trade-off for selecting smoothing parameters.

Teaching and learning methods

36 Lectures and/or problem classes.

Resources & Reading list

Textbooks

Faraway, J. J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models.. CRC Press.

Wood, S. N. (2017). Generalized Additive Models: An Introduction with R. CRC Press.

Demindenko, E. (2013). Mixed Models: Theory and Applications with R. Wiley..

Assessment strategy

The assessment for the repeat candidates will be based completely on the final examination.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

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