Design of Experiments STATS4008

  • Academic Session: 2018-19
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes

Short Description

To provide an introduction to the statistical aspects of designing experimental studies, and to introduce associated methods of statistical analysis.

Timetable

20 lectures (2 each week in Weeks 1-10 of Semester 2)

4 tutorials (fortnightly)

Requirements of Entry

The normal requirement is that students should have been admitted to an Honours- or Master's-level programme in Statistics.

Excluded Courses

STATS5017 Design of Experiments (Level M)

STATS3013 Statistics 3D: Design of Experiments

Co-requisites

Normally, the courses prescribed in the Honours- or Master's-level programme to which the student has been admitted.

Assessment

90-minute, end-of-course examination (100%)

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Course Aims

To provide an introduction to the statistical aspects of designing experimental studies; to introduce associated methods of statistical analysis; to discuss optimality concepts for the design of experiments; to review basic sampling techniques and their statistical analyses.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ Describe the difference between an experiment and an observational study

■ Explain the principles of randomisation, replication and stratification, and understand how they apply to practical problems.

■ Understand the general theory of factorial and block designs and to find appropriate designs for specific applications.

■ Evaluate designs using common optimality criteria and used them to critically compare competing designs.

■ Derive estimators of population means and their standard errors for different types of random sampling, and use these to construct optimal sampling strategies.

■ Apply theory and methods to a variety of applications.

Minimum Requirement for Award of Credits

None.