2017/18 Taught Postgraduate Module Catalogue

EPIB5023M Introduction to Modelling

15 creditsClass Size: 30

Module manager: Professor Mark Gilthorpe
Email: m.s.gilthorpe@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2017/18

Pre-requisite qualifications

Academic entry requirements

Normally a first degree in a science allied with medicine, including biology, ecology, biochemistry, statistics, mathematics, computing, psychology, economics or biomedical science (at least 2:2). We will also consider working experience (two years or more) of research in a quantitative subject area.

English language requirements

An overall score of 7.0 on IELTS (International English Language Testing System) with at least 6.0 in writing and no other skill below 6.5; from a TOEFL paper based test the requirement is a minimum score of 600, with 4.5 in the Test of Written English (TWE); from a TOEFL computer based test the requirement is a minimum score of 250, with 4.5 TWE; from a TOEFL Internet based test the requirement is a minimum score of 100, with 25 in the "Writing Skills" score.

This module is not approved as an Elective

Module summary

The module is designed to give students a comprehensive introduction to linear modelling and equip them with the skills and knowledge necessary to analyse various different outcome data types. By the end of the module students will be able to identify suitable linear models for analysing a variety for different outcome types; fit a linear model using statistical software including selection of model parameters; compare between models and assess the appropriateness or otherwise of the fitted model.

Objectives

To provide a comprehensive understanding of the principles (model theory, limitations, assumptions) underpinning the use of (generalised) linear modelling in medicine and health;
To enable students to apply their knowledge to a range of situations and datasets such that they can fit a variety of linear models and compare model fit between them;
To critically appraise the use of such models within the literature.

Learning outcomes
By the end of this module the student should be able to:

- Describe the principles of linear modelling including a knowledge of which type of model is appropriate for particular outcome data types;
- Explain the principles of the method of least squares and maximum likelihood for estimating parameters in a linear model;
- Formulate regression models on a computer;
- Compare and contrast different models;
- Appraise a range of methods for including/excluding parameters in a model and be able to apply this knowledge when fitting a linear model on a computer;
- Formulate tests for parameter fits;
- Explain the principle of parsimony;
- Apply a variety of diagnostic and validity checks of model fits to arrive at the most appropriate model;
- Construct generalised linear models for prediction and report the predictions;
- Critically appraise the use of linear models.

Skills outcomes
Statistical analysis skills. Practical modelling skills and critical evaluation of the use of linear models

Syllabus

- Introduction to linear models;
- Introduction to statistical software appropriate for fitting linear models;
- Correlation and simple linear regression;
- Multiple linear regression, including maximum likelihood estimation;
- Model fitting, parameter estimation and interpretation;
- Model diagnostics;
- Extending the linear model: Generalised linear modelling including logistic regression analysis and Poisson regression.

Teaching methods

Delivery type	Number	Length hours	Student hours
Class tests, exams and assessment	1	1.50	1.50
Lecture	11	1.00	11.00
Practical	22	1.00	22.00
Private study hours			115.50
Total Contact hours			34.50
Total hours (100hr per 10 credits)			150.00

Private study

At least 4 hours per week of private study of additional course materials to support lectures and tutorial work. In addition students are expected to spend 34 hours on each of the two assignments.

Opportunities for Formative Feedback

The weekly practical sessions will form the basis of continuous student monitoring. In addition the two assignments which are staggered during the course will allow the module leader to monitor student progress and assess whether there are particular students in need of additional help, or whether there are particular topics that require further elucidation.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	Project report - 1,000 words	25.00
Report	Project report - 1,000 words	25.00
Total percentage (Assessment Coursework)		50.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams

Exam type	Exam duration	% of formal assessment
Standard exam (closed essays, MCQs etc)	2 hr 00 mins	50.00
Total percentage (Assessment Exams)		50.00

The examination will include a range of question types, including MCQs and short answers.

Reading list

The reading list is available from the Library website

Last updated: 07/07/2015

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