MATH6157 Applied Statistical Modelling
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 and, briefly, Python. A variety of exemplar applications and data sets will be presented.
Aims and Objectives
To introduce, via a hands-on approach, the basic concepts and principals in statistical modelling in a computational paradigm.
Having successfully completed this module you will be able to:
- After taking this module, students should understand - how to use R to fit, explore and exploit a variety of statistical models
- After taking this module, students should understand - why statistical modelling is important
- Demonstrate an ability to concisely convey technical results
- After taking this module, students should understand the terminology and statistical principles associated with modelling
- After taking this module, students should understand sufficient theory to deal with simple examples and have gained practical hands-on experience in more complex examples
Introduction and revision - Python and R, and their 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
|Total study time||150|
Resources & Reading list
Wu, C.F.J. and Hamada, M. (2011). Experiments: planning, analysis and optimisation.
Gelman, A. and Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models.
Computer requirements. Python and R (freely available)
Faraway, J. (2014). Linear Models with R.
Davison, A.C. (2008). Statistical Models.
Wood, S.N. (2006). Generalized Additive Models: An Introduction with R..
Repeat type: Internal & External