Credit hours
In-class work per week |
Practice per week |
Credits |
Duration |
Total |
10 |
5 |
6 |
3 weeks |
90 hours |
Instructor
Gerson Barreto Mourão
Objective
To train researchers from diverse areas of biosciences in the fundamental principles of experimental design and its analysis through a modern computational workflow (R, Python, SAS). To introduce the use of Artificial Intelligence (AI) tools as assistants for assumption checking, interpretation of results, and ensuring research reproducibility.
Content
1. Scientific Research and Experimental Design; 2. Introduction to the Computational Environment for Data Analysis; 3. Fundamental Statistical Concepts; 4. Completely Randomized Design (CRD); 5. ANOVA Assumptions and the Role of AI; 6. Multiple Comparisons and Contrasts; 7. Randomized Complete Block Design (RCBD); 8. Latin Square Design (LSD); 9. Regression Analysis in Experimental Designs; 10. Communicating Results
Bibliography
BARBIN, D., 1994. Planejamento e análise estatística de experimentos agronômicos, Piracicaba, SP - 135 p. MONTGOMERY, D.C. 1991. Design and analysis of experiments. Jonh Wiley and Sons, N.Y., 649 p. MEAD, R. & CURNOW, R.N. Statistical Methods in Agriculture and Experimental Biology, 3ª Edição. Champman & Hall/CRC, USA. 2002, 304 p. STEEL, R. G. D. e T. H. TORRIE, 1980. Principles and procedures of statistics. McGraw-Hill, Nova York, 481 p.; BOLKER, Benjamin M. Ecological Models and Data in R. Princeton: Princeton University Press, 2008.; LITTELL, Ramon C. et al. SAS for Mixed Models. 2. ed. Cary: SAS Institute Inc., 2006.; GELMAN, Andrew; HILL, Jennifer; VEHTARI, Aki. Regression and Other Stories. Cambridge: Cambridge University Press, 2020.; KUHN, Max; SILGE, Julia. Tidy Modeling with R. 2022.; LONG, Jeffrey D. Longitudinal Data Analysis for the Behavioral Sciences Using R. Thousand Oaks: SAGE Publications, 2022. Scientific journals as sources for case studies: Journal of Animal Science and Journal of Dairy Science