Course Overview

The proposed program includes experimental designs used in the agricultural experimentation whose structure requires special attention due to the methodology applied for the analysis of the obtianed data.

Learning Achievement

Aims to provide a solid methodological basis for the use of models in the analysis of continuous data and in research, involving concepts of matrix algebra and statistical inference, teaching Hasse diagrams, linear models for incomplete data using the theory of mixed models, including the estimation techniques, checking of model fitting, diagnostics, inference and confidence intervals.

Competence

Course prerequisites

Grading Philosophy

Evaluation tests and Seminars

Course schedule

Hasse Diagrams. Unbalanced cross-classifications. Split-plot experiments. Split-Block experiments. Incomplete block designs. Lattice squares. Groups of experiments. Groups of experiments with common treatments (augmented block). Introduction to mixed models.

Course type

Online Course Requirement

Instructor

Sonia Maria de Stefano Piedade, Clarice Garcia Borges Demetrio, Taciana Villela Savian

Other information

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