The Ph.D. course will be given as a distance course from 1 November to 1 December 2022.
Prerequisites: Statistics I or equiv.
Objective, including learning outcomes
The objective of the course is to give an overview of the basic principles behind design and analysis of factorial experiments. On completion of the course, the student will be able to:
• describe basic principles in experimental design and specify analysis of variance (ANOVA) models including conditions and assumptions
• select an appropriate ANOVA model for a given experimental design
• carry out ANOVA using the statistical software R or SAS
• interpret and evaluate results correctly and draw reasonable conclusions
• clearly and concisely communicate results and conclusions
The course will cover the following topics:
• Analysis of experiments with one or more fixed and random factors, randomized block designs, crossed and nested factors.
• Multiple comparisons.
• Analysis of residuals.
• Non-parametric ANOVA, Kruskal–Wallis’ and Friedman’s tests.
• Mixed-effects models