Analysis of Variance


1. To design and conduct an experiment that exhibits both a treatment design and randomization design that allow for the testing of differences between the levels of a single or multiple treatment of interest.
2. Given a description of an experiment, to identify common design elements.
3. To specify an appropriate statistical model for observations resulting from a designed experiment exhibiting the elements in Learning Objective 2.
4. To Identify estimable terms in a statistical model for an experiment, find the least squares estimator of estimable terms, and specify the statistical distribution of the estimator.
5. To conduct and correctly interpret statistical hypothesis tests for the overall effect of a treatment and for the effects of contrasts.
6. To employ methods for multiple comparisons to control the experiment error rate when multiple hypothesis tests are conducted.
7. To examine model assumptions using residual plots and a description of the experiment.

General characterization





Responsible teacher

Docente a designar


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English




- Dean, A., Voss, D. (1999), Design and Analysis of Experiments. Springer

Teaching method

The curricular unit is based on theoretical and practical lessons. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step applications (with and without software), questions and answers. The sessions include presentation of concepts and methodologies, solving examples, discussion and interpretation of results. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results. A set of exercises to be completed independently in extra-classroom context is also proposed.


Evaluation method

1st call: project (40%), first round exam (60%)
2nd call: final exam (100%)

Subject matter

1. Principle and techniques
2. Planning experiments
3. Inferences for Contrasts and Treatment Means
4. Checking Model Assumptions