MATH6157 Applied Statistical Modelling
Module Overview
This module will introduce important general aspects of statistical modelling and some fundamental aspects of data collection for computer and simulation experiments. A broad range of commonly-used statistical models will be encountered, and used to demonstrate both general principles and specific examples of modelling techniques in R. A variety of exemplar applications and data sets will be presented.
Aims and Objectives
Module Aims
To introduce, via a hands-on approach, the basic concepts and principals in statistical modelling in a computational paradigm.
Learning Outcomes
Learning Outcomes
Having successfully completed this module you will be able to:
- understand how to use R to fit, explore and exploit a variety of statistical models
- should understand why statistical modelling is important
- demonstrate an ability to concisely convey technical results
- understand the terminology and statistical principles associated with modelling
- understand sufficient theory to deal with simple examples and have gained practical hands-on experience in more complex examples
Syllabus
Introduction and revision - R, and its interface - Data input, plotting and summaries - Standard statistical distributions - Principles of statistical inference - Likelihood Regression: linear and generalised linear modelling - Model construction and estimation - Model selection and information criteria - Shrinkage regression (Lasso and ridge methods) Random effects, mixed models, and data with complex correlation structures - Grouping structures in data - Interpretation of random effects and mixed models - Discrete data and generalised linear mixed models - Estimation of mixed models - Autoregression models Smoothing and nonparametric regression - Kernel density estimation - Splines and penalised splines - Generalised additive models - Linear smoothing Data collection for computational studies - Fundamentals of design of experiments - Computer and simulation experiments - Latin hypercube sampling
Learning and Teaching
Teaching and learning methods
Teaching methods - 24 lecture hours - 24 computer workshop hours Learning methods - Individual study facilitated via weekly worksheets to support lecture material and assessed coursework - Supervised problem solving via computer lab sessions
Type | Hours |
---|---|
Independent Study | 102 |
Teaching | 48 |
Total study time | 150 |
Resources & Reading list
Faraway, J.J. (2016). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models.
Gelman, A. and Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models.
Wood, S.N. (2006). Generalized Additive Models: An Introduction with R..
Computer requirements. R (freely available)
Wu, C.F.J. and Hamada, M. (2011). Experiments: Planning, Analysis and Optimisation.
Davison, A.C. (2008). Statistical Models.
Faraway, J. (2014). Linear Models with R.
Assessment
Summative
Method | Percentage contribution |
---|---|
Class Test | 50% |
Class Test | 50% |
Referral
Method | Percentage contribution |
---|---|
Coursework assignment(s) | 100% |
Repeat Information
Repeat type: Internal & External