Flexible Regression

Learning Outcomes

Learning Outcomes

Having successfully completed this module you will be able to:

• Understand the theoretical underpinnings of the discussed modelling methods, and their implementation in appropriate statistical software.
• 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.

• 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.

36 Lectures and/or problem classes.

Formative

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

Assignments and problem sheets

• Assessment Type: Formative
• Feedback:
• Final Assessment: No
• Group Work: No

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.