as well as type of design.
The module includes enough about the analysis of data from experiments to show
what has to be considered at the design stage.
It also includes considerations of
consultation with the scientist and interpretation of the results.
Syllabus

Introduction to concepts in the design of real comparative experiments.

Randomization, replication, power.

Simple linear model, orthogonal subspaces, analysis of variance.

Blocking. Fixed effects or random effects. Orthogonal designs.

Factorial designs. Main effects and interactions. Control treatments.

Rowcolumn designs. Latin squares.

Observational units smaller than experimental units. False replication.

Splitplot designs. Treatment effects in different strata.

Structures defined by families of orthogonal factors.
Eigenspaces of highly structured variancecovariance matrices.

Showing factors on a Hasse diagram. Using the Hasse diagram to
calculate degrees of freedom and allocate treatment effects to strata.
Skeleton analysis of variance.
Intended Learning Outcomes
On completion of the module, students should be able to do the following.