MTHM506 | 2020/1 | University of Exeter

MODULE TITLE	Statistical Data Modelling	CREDIT VALUE	15
MODULE CODE	MTHM506	MODULE CONVENER	Dr Dorottya Fekete (Coordinator)

DURATION: TERM	1	2	3
DURATION: WEEKS	5 (October start) / 0 (January start)	0	0 (October start) / 5 (January start)

Number of Students Taking Module (anticipated)	50

DESCRIPTION - summary of the module content

Statistical modelling lies at the heart of modern data analysis. Simple statistical models include the regression and multiple regression familiar from most foundation courses in statistics. This module develops the ideas of regression and multiple regression and places them within the broader context of the Generalised Linear Model. Extensions to this framework involving random effects, Generalised Linear Mixed Models, Generalised Additive Models are then developed. We will use the statistical software R in ‘hands-on’ practical sessions where you will learn how to implement the theoretical techniques learnt within the course as part of practical data analysis

AIMS - intentions of the module

The introduction of Generalised Linear Models (GLMs) provides a unified framework for a wide variety of regression models, statistical learning and data analysis techniques. This module will describe the underlying theory of GLMs and provide an introduction to the application of commonly used GLMs. This is followed by an exploration of modern statistical modelling techniques, including nonparametric and semi-parametric formulations (GAMs), and hierarchical modelling and random effects (GLMMs).

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

Module Specific Skills and Knowledge:

Show understanding of the many different types of statistical data structure that commonly occur and the need to model relationships in such data appropriately;

Demonstrate awareness of, and ability to apply, the unifying power and flexibility of the generalised linear model (GLM) as a means of describing relationships in data;

Reveal awareness of, and ability to apply, related modern developments in statistical modelling techniques, including nonparametric and semi-parametric formulations (GAMs), hierarchical modelling and random effects (GLMMs)=

Utilise appropriate software and a suitable computer language for advanced modelling of data

Discipline Specific Skills and Knowledge:

Demonstrate understanding and appreciation of, and aptitude in, the advanced mathematical modelling of stochastic phenomena and its usefulness;

Personal and Key Transferable/ Employment Skills and Knowledge:

Show advanced data analysis skills and be able to communicate associated reasoning and interpretations effectively in writing;

Apply relevant computer software competently;

Use learning resources appropriately;

Exemplify self-management and time-management skills;

Gain experience in problem solving using data analysis.

SYLLABUS PLAN - summary of the structure and academic content of the module

- Introduction to advanced statistical modelling of relationships between variables: the need to move beyond the normal theory linear model (motivation and examples);

- Value of general modelling frameworks and paradigms;

- Generalised linear models (GLMs): definition, maximum likelihood estimation, iteratively reweighted least squares, inference in the GLM, GLM selection, GLM diagnostics;

- Examples of GLMs, normal linear models as GLMs, Bernoulli and binomial data, Poisson count data, contingency tables, multinomial data, other GLMs, mean dispersion relationships and overdispersion, quasi-likelihood;

- Generalised additive models (GAMs): parametric versus nonparametric and semi-parametric models, kernel, spline and local polynomial estimation methods, additive models and generalized additive models;

- Hierarchical models and random effects: the concepts of random effects and hierarchical (multilevel) modelling, normal theory linear mixed models, generalised linear mixed models (GLMMs).

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	30	Guided Independent Study	120	Placement / Study Abroad

DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS

Category	Hours of study time	Description
Scheduled learning and teaching	20	Lectures
Scheduled learning and teaching	10	Hands-on practical sessions
Guided Independent Study	36	Post lecture study and reading
Guided Independent Study	84	Formative and summative coursework preparation

ASSESSMENT

FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade

Form of Assessment

Size of the assessment e.g. duration/length

ILOs assessed

Feedback method

Unassessed Practical Modelling Exercises 1

10 hours

1,2,4-10

Verbal, in class

Unassessed Practical Modelling Exercises 2

10 hours

1,3,4-10

Verbal, in class

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of credit	Size of the assessment e.g. duration/length	ILOs assessed	Feedback method

Coursework – practical modelling exercises and theoretical problems 1	25	10 hours	1, 2, 4-10	Written and oral
Coursework – practical modelling exercises and theoretical problems 2	25	10 hours	1, 3, 4-10	Written and oral
Coursework – data analysis project	50	20 hours	1-10	Written and oral

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)

Original form of assessment	Form of re-assessment	ILOs re-assessed	Time scale for re-assessment
As above			Ref/Def Period

RE-ASSESSMENT NOTES

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.

RESOURCES

INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Reading list for this module:

Type	Author	Title	Publisher	Year	ISBN
Set	Krzanowski, W.J.	An Introduction to Statistical Modelling	Wiley	1998	978-0470711019
Set	Aitkin, M., Francis, B., Hinde, J. and Darnell, R.	Statistical Modelling in R	Oxford University Press	2008	9780199219131
Set	Crawley, M.J.	The R Book	Wiley	2007	9780470510247
Set	Faraway, J.J.	Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models	Chapman & Hall	2006	158488424X
Set	Wood, Simon N.	Generalized Additive Models: An Introduction with R	Chapman & Hall/CRC	2006	978-1584884743
Set	Gelman, A. and Hill, J.	Data Analysis Using Regression and Multilevel/Hierarchical Models	Cambridge University Press	2007	052168689X

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Monday 14th September 2020	LAST REVISION DATE	Monday 14th September 2020

KEY WORDS SEARCH	None Defined

Statistical Data Modelling - 2020 entry