Statistical Design and Analysis of Experiments
Overall Course Objectives
To acquaint the students with efficient statistical methods that may serve as basis for the planning of experiments and research activities. The methods aim at providing precise and reliable data and moreover to reduce the experimental work as much as possible. Methods for reduction of the influence from noise factors on experimental results are presented.
See course description in Danish
Learning Objectives
- formulate an adequate experimental design for an experiment in the laboratory or under similar more or less controlled conditions.
- assess advantages and disadvantages of alternative experimental designs, for example with respect to randomization and/or blocking.
- formulate a correct mathematical model for data obtained froma given experimental design.
- test and estimate the effects in models for experiments with both deterministic and random effects.
- do detailed analyses of factorial effects by means of adequate contrasts and/or methods for grouping of mean responses.
- to construct experimental designs for factorial experiments with many factors each at (most often) two levels by means of block confounding and reduction through effect confounding.
- use special methods for statistical analysis of data from block confounded and/or reduced many-factor designs.
- use and carry out the statistical analysis of data from experiments with measurable sources of experimental noise (covariates).
- determine the necessary number of measurements in simple designs.
Course Content
Main principles in experimental design: Randomisation and blocking. Balanced designs, square-designs, one or more blocking variables. Balanced incomplete block designs. Factorial designs, blocking in factorial designs, fractional factorial designs and confounding. Multiple regression analysis and analysis of covariance.
Recommended prerequisites
02402, or a similar course in elementary statistics
Teaching Method
Lectures and tutorials.
Faculty
Remarks
The course is primarily aimed at students that are going to design experiments and/or analyse experimental data, but the general methods presented are also suited for sharpening the critical sense in experimental work, method development and empirical model building.