EXPERIMENTAL DESIGN - 2024/5

Module Overview

Fundamental topics in the design and analysis of experiments are introduced in this module. For a variety of statistical models, the structure of the model and applications are covered. Particular attention is given to practical issues. Statistical software is used to ensure that the emphasis is on methodological considerations rather than on calculation.

 

There are no pre-requisites for the module but students who have not taken MAT2002 General Linear Models will need to do some initial reading.

Module provider

Mathematics & Physics

Module Leader

GODOLPHIN Janet (Maths & Phys)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 6

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content

Indicative content includes:

• General Concepts: Principles of design and strategy of experimentation; Complete designs: m-way classification.


• Designs Involving Blocking: Randomized block designs; Incomplete block designs; Balance; Design construction.


• Further Topics Involving Blocking: Optimality criteria; Connectivity.


• Factorial Designs: Principles and advantages of factorial designs; Two level factorial systems; Fractional factorial designs and aliasing; Confounding factorial effects with block effects.

A selection from specialised topics:

• Analysis of covariance; Binary response data; Crossover designs and carryover effects.

Assessment pattern

Alternative Assessment

N/A

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate: 

• Analytical ability by solution of unseen problems in examination. 


• Subject knowledge through the recall of key definitions, theorems and their proofs.


• An understanding of practical considerations when designing an experiment.


• The ability to analyse data, to interpret the analysis and report comprehensively on the results.

Thus, the summative assessment for this module consists of:

• One assessed coursework (completed in groups), worth 20% of the module mark, corresponding to Learning Outcomes 3, 5 and 6.


• A synoptic examination (2 hours), worth 80% of the module mark, corresponding to Learning Outcomes 1 to 4 and 6.

 

Formative assessment
There are two formative unassessed courseworks over an 11 week period, designed to consolidate student learning. 
 

Feedback
Written feedback is provided to students for both formative unassessed courseworks and the assessed coursework. Any issues with the unassessed coursework are discussed in lectures. Likewise, verbal feedback on the exercise sheets is given in lectures. Before starting data collection, coursework groups submit a plan of the experiment for approval and feedback. Each group also completes a university ethics form before starting the coursework. Students receive verbal feedback during computer lab sessions: this is particularly useful in providing guidance for the assessed coursework which is submitted after the final computer lab session.

Module aims

• Provide students with a detailed understanding of the principles of experimental design.
• Give students practical experience of planning, conducting and analysing an experiment using a BIBD, and practical experience of analysing data and reporting the results.
• Equip students with the tools and techniques to be able to design and analyse appropriate experiments in a range of situations.
• Cover the theory behind the analysis of data from various models.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to: 

Equip students with the knowledge of the principles and theory of experimental design and with practical experience in experimental design and analysis.

The learning and teaching methods include:

• Three one-hour lectures per week for eleven weeks, with typeset notes to complement the lectures. The lectures provide a structured learning environment with opportunities for students to ask questions and to practice methods taught.


• Three one-hour computer lab sessions in which students gain practical experience of analysing data sets using R.


• Students are provided with exercise sheets aimed at reinforcing their learning. These sheets allow students to tackle questions at their own pace outside of scheduled teaching sessions. Lecture time is assigned to help students tackle any challenges they might face.


• There are two unassessed courseworks to provide students with further opportunity to consolidate learning. Students receive individual written feedback on these as guidance on their progress and understanding.


• Lectures may be recorded. Lecture recordings are intended to give students the opportunity to review parts of the session that they might not have understood fully and should not be seen as an alternative to attendance at lectures.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MAT3021

Other information

The School of Mathematics and Physics is committed to developing graduates with strengths in Digital Capabilities, Employability, Global and Cultural Capabilities, Resourcefulness and Resilience and Sustainability. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Digital Capabilities: The computer lab sessions and assessed coursework enable students to develop proficiency in digital data manipulation and modelling using the statistical software R.

Employability: MAT3021 enhances employability by fostering problem-solving skills and proficiency in data analysis. These skills are highly sought after, especially in the fields of quality assurance and in research and development.

Global and Cultural Capabilities: Data sets and examples used in MAT3021 arise from a selection of countries, cultures and environments, thus aiding students in the development of their cultural awareness.

Resourcefulness and Resilience: The assessed coursework of MAT3021 requires students to overcome the practical difficulties encountered when executing an experimental design. Conquering these challenges nurtures resourcefulness and cultivates resilience in students.

Sustainability: Students encounter experiments relating to the optimization of industrial processes. The identification of experimental settings that maximise yield, emphasises efficient resource utilisation and so aligns with sustainability.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2024/5 academic year.