Experimental Design

Aim

The course is intended to give the knowledge and understanding to experimental design and analysis of factorial experiments. The emphasis is on design of experiments, selection of model, analysis of observed data, ability to interpret the results and to draw conclusions. Knowledge of alternative nonparametric methods is also essential.

By the end of the course, the student should be able to:

• understand the need of different designs and how to analyze them;
• design factorial experiments of different types using appropriate randomization;
• choose a suitable model to describe observed data considering the design of the experiment that generated the data, then perform an appropriate analysis and draw conclusions by means of hypothesis testing and construction of confidence intervals;
• by the use of observations, make multiple comparisons of parameters with a given simultaneous confidence level;
• check model adequacy and need of transformation of data;
• analyze real data using designs and methods discussed in the course.

Content

Repetition of Linear models and Analysis of Variances (ANOVA). Model validation. Single-, two- and multifactor experiments in theory and practice. Pairwise and multiple comparisons. Complete randomized and block designs. Complete block designs, such that Latin Squares, Graeco-Latin Squares, or more general Hyper-Latin Squares. Incomplete block designs, e.g., Balanced Incomplete Block Design, Youden Squares design (incomplete Latin Squares), Partially Balanced Incomplete Block Designs, Cyclic Designs, and Lattice designs. Complete 2^k factorial designs and fractional factorial designs. Response surface methods. Analysis of data by using statistical software.