# Data Analytics MSc

## Design of Experiments (Level M) STATS5017

• School: School of Mathematics and Statistics
• Credits: 10
• Level: Level 5 (SCQF level 11)
• 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)

### Excluded Courses

STATS4008 Design of Experiments

STATS3013 Statistics 3D: Design of Experiments

### Assessment

120-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 and advanced 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 and derive the general theory of factorial and block designs and to find appropriate designs for specific applications.

■ Explain the theoretic basis of common optimality criteria, and used them to evaluate and 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.

None.