Data Analytics MSc
Principles of Designed Experiments and Surveys (Level M) STATS5017
- Academic Session: 2026-27
- School: School of Mathematics and Statistics
- Credits: 10
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
This course presents concepts and accepted methodologies for the design and analysis of experiments and their applications in treatment comparison, experimental screening, and response optimization. These include blocked and (fractional) factorial designs, response surface methodology for related factors, and optimality criteria to compare competing designs. Concepts and methods are illustrated in various examples drawn from medical, agricultural, and industrial contexts. The last part of the course covers principles of survey sampling, where estimators of population means and proportions along with associated uncertainty measures are derived for different sampling schemes. It further enables students to engage in deeper reading on advanced notions of survey sampling theory, including advanced estimation methods for simple random sampling and proportional-to-size sampling.
Timetable
20 lectures (2 each week in Weeks 1-10 of Semester 2)
4 tutorials (fortnightly)
Excluded Courses
STATS4008 - Design of Experiments
Assessment
120-minute, end-of-course examination (100%)
Main Assessment In: April/May
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, blinding, 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.
■ Apply and derive estimators of population means and their standard errors under a range of random sampling designs, including simple, stratified, and cluster sampling, and use these to construct efficient sampling strategies for practical survey contexts.
■ Apply theory and methods to a variety of applications.