Lecturer(s): Prof. Mark Steel and Dr Samuel Touchard
Important: If you decide to take ST305 you cannot then take ST410. Bear this in mind when planning your module selection. Recall: an Integrated Masters student must take at least 120 CATS, of level 4+ modules over their 3rd & 4th years.
Prerequisite(s): Either ST218/219 Mathematical Statistics A&B or ST220 Introduction to Mathematical Statistics
Commitment: 30 one-hour lectures, plus weekly one-hour seminars / practicals. This module runs in Term 2.
Background: Designed experiments are used in industry, agriculture, medicine and many other areas of activity to test hypotheses, to learn about processes and to predict future responses. The primary purpose of experimentation is to determine the relationship between a response variable and the settings of a number of experimental variables (or factors) that are presumed to affect it. Experimental design is the discipline of determining the number and order (spatial or temporal) of experimental runs, and the setting of the experimental variables.
Content: This is a first course in designed experiments. The elementary theory of experimental design relies on linear models, while the practice involves important eliciting and communication skills. In this course we shall see how the theory links common designs such as the randomised complete block and split-plot to the underlying model. The course will commence with a review of linear model theory and some simple designs; we shall then examine the basic principles of experimental design and analysis, e.g. the concepts of randomisation and replication together with the blocking in designs and the combination of experimental treatments (factorial structure). Classical design structures are developed through the separate consideration of block and treatment structure, and the use of analysis of variance to explore differences between treatments for different types of design is explored. Throughout, diagnostic and analysis methods for the examination of practical experiments will be developed. A significant part of the course will be spent developing aspects of factorial design theory, including the theory and practice of confounding and of fractional designs. We will see how the exigencies of design in an industrial context have led to further theory and different emphases from classical design. This will include the use of regression in response surface modelling. Further topics such as repeated measures, non-linear design and optimal design theory may be included if time allows. Practical examples from many different application areas will be given throughout, with an emphasis on analysis using R.
Aims: This course aims to give students a sound understanding of experimental design, both theoretical and practical. The course will explore the method of analysis of variance and show how it is structurally linked to particular types of design. The combinatoric properties of designs will be explored, and the impact of computers on classical design considered. Some exploration of the matrix theory of design will also be undertaken.
Objectives: By the end of the course students will be able to:
Other books will be referred to through the module.
Assessment: Two assignments worth 10% each and 80% by 2-hour examination.
Deadlines: Assignment 1: Week 7 of Term 2. Assignment 2: Week 1 of Term 3. Other exercises will be provided and discussed during the seminars.
Feedback: Feedback on both assignment 1 and 2 will be returned after 2 weeks, following submission. Students will also receive feedback to worksheet examples during practical classes.