This unit provides the analysis of the evolution and practice of Experimental Design (PE) over a century of existence, from Sir Ronald Fisher (2018) to the present, when skills acquisition and training require the use of new computational and digital technologies. New professional activities emerge and this UC promotes the exploration of trends and topics where PE offers competitive advantages.
At the end of this course students will have skills that allow them to:
-Know how to select models appropriate to each problem;
-Reveal proficiency in hypothesis generation and statistical analysis with R software in real and simulated situations, including graphical visualization and interpretation of results;
-Know and develop classical and advanced PE models exploring properties, links and extensions;
-Use ANOVA and analyze contrasts;
-Apply and develop Response Surface Methodologies in modeling and analysis of optimization problems.
1. Experimental Design classical models
Importance and need of Experimental Design, Complete and Incomplete Block Design Models. Latin, Greco-Latin and Hyper-Greco-Latin Square Designs.
Design Optimality Criteria.
2. Standard Designs for 1st and 2nd Degree Models
Hadamard Designs, Factorial Designs and Fractional Factorial Designs. Confounding Systems for Two-Level Factorials, Composite Plans, Doehlert and Box Behnken Plans. Canonical Analysis.
3. Advanced Experimental Design Methods and Models
Response Surface Methodologies, Cross-linked Designs and Crossover Designs. 4. Experimental Design with Real and Simulated Data
New paradigms of Experiment Planning in the 21st century. Experimental Design Applications in classical and emerging areas using R software.
 Dean, A., Morris, M. and Stufken, J., Bingham, D. (2015).Handbook of Designand Analysis of Experiments. Chapman & Hall/CRC Handbooks of Modern Statistical Methods Published June 26, 2015. ISBN 9781466504332
 Hinkelmann, K and Kempthorne, O. (2005). Design and Analysis of
Experiments, Volume 2: Advanced Experimental Design. ISBN: 978-0-471-55177-5.
 Lawson, J. (2015). Design and Analysis of Experiments with R, CRC Press.
 Montgomery, D.C. (2017). Design and Analysis of Experiments, 9th Ed., Wiley.
Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (at least 60%) and final evaluation (at most 40%). Further information is detailed in the Learning Agreement of the course unit.