MT4614 Design of Experiments

Academic year

2024 to 2025 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

15

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 10

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Availability restrictions

Not automatically available to General Degree students. Module capped at 21 students.

Planned timetable

9.00 am Mon (odd weeks), Wed and Fri

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module coordinator

Prof R A Bailey

Prof R A Bailey
This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module introduces a wide range of features that occur in real comparative experiments. The applications include trials of potential new medicines by the pharmaceutical industry; comparisons of new varieties of wheat for bread-making; evaluating different machine settings in industry. Issues include whether and how to partition the experimental material into blocks (for example, do old and young people respond to this drug differently?); how much replication to use (too much experimental material may be a waste of resources, but too little will not give meaningful results); as well as type of design. The module includes enough about the analysis of data from experiments to show what has to be considered at the design stage. It also includes considerations of consultation with the scientist and interpretation of the results.

Relationship to other modules

Pre-requisites

BEFORE TAKING THIS MODULE YOU MUST PASS MT2508 AND PASS MT3501

Assessment pattern

2-hour Written Examination = 80%, Presentation = 10%, Coursework = 10%

Re-assessment

Oral examination = 100%

Learning and teaching methods and delivery

Weekly contact

2.5 lectures (x 10 weeks) and either tutorial or practical (x 10 weeks).

Scheduled learning hours

35

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

115

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Have a meaningful discussion with a non-mathematical scientist, including asking questions about the purpose of the experiment, the meaning of technical scientific words, any operational restrictions imposed by time or space or staffing or money, and later explain to the scientist the outcome of the data analysis in terms that they can understand
  • Construct and appropriately randomize certain standard classes of design, including completely randomized designs, complete-block designs, orthogonal row-column designs and split-plot designs
  • Understand and state relevant definitions and theorems, prove those theorems and variants of those theorems
  • Understand and explain the concepts of main effects and interaction in factorial experiments, and their extension to factorial experiments with an extra control treatment
  • Write and understand the appropriate linear model, interpret this as a collection of expectation subspaces and a variance-covariance matrix with known eigenspaces, use these to sketch out how to do the data analysis by hand and then do it using the appropriate code in R, and verify that the output from R matches the format that is expected
  • Understand and use a Hasse diagram to show the relationships between factors, including finding out which stratum each treatment subspace is in and identifying any pseudoreplication