Teaching GuideTerm
Faculty of Computer Science
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Mestrado Universitario en Técnicas Estadísticas (Plan 2019)
 Subjects
  Design and Analysis of Experiments
   Contents
Topic Sub-topic
1. Basic principles of experimental design. 1.1. Introduction: Advantages of planning an experiment. Variability sources.
1.2. Basic principles in experimental design.
1.3. Step by step guide to the experimental planing process. A real example.
1.4. Some standard experimental designs.
2. Designs with one source of variation. 2.1. Introduction.
2.2. Randomization. Model for a completely randomized design: Estimation of parameters, one-way analysis of variance, inference on contrasts and means.
2.3. Methods of multiple comparisons.
2.4. Checking the adequacy of the model.
2.5. Alternative approaches.
3. Designs with several sources of variation. 3.1. Introduction.
3.2. Randomization. The meaning of interaction. Complete model. Main effects model.
3.3. Estimation, analysis of variance, inference on contrasts.
3.4. Sample sizes.
3.5. Checking the adequacy of the model.
4. Analysis of covariance. 4.1. Introduction.
4.2. Mathematical models.
4.3. Estimation, analysis of variance, inference on contrasts.
4.3. Checking the adequacy of the model.
5. Random effects models and mixed models. 5.1. Random effects: variance components. Examples.
5.2. Mathematical models for random effects models: Estimation and analysis of variance.
5.3. Sample sizes.
5.4. Checking the adequacy of the model.
5.5. Mixed models: los mixtos: Estimation and analysis of variance.
6. Block designs. 6.1. Basic concepts.
6.2. Complete block designs: Models, estimatin, analysis of variance, inference on contrasts.
6.3. Incomplete block designs: Balanced incomplete block designs; group divisible designs; cyclic designs. Models, estimation, analysis of variance, inference on contrasts.
6.4. Row-column design: Latin square designs, Youden designs, cyclic and other row-column designs. Models, estimation, analysis of variance, inference on contrasts.
6.5. Alternative approaches.
7. Nested designs. 7.1. Introduction.
7.2. Nested designs in two stages..
7.3. Nested designs in m stages.
7.4. Models including both nested and crossing sources of variation.
8. Split-plot dsigns. 8.1 Introduction: Motivation and examples.
8.2. Mathematical modrls.
8.3. Estimation and analysis of variance with complete blocks.
9. Designs with repeated measures. 9.1. Introduction: Experimental setup.
9.2. Dependence structures for repeated measures.
9.3. Mauchly's test of sphericity.
9.4. Univariate and multivariate analysis.
10. Factorial designs at two levels.
 10.1. Two levels designs with two factors.
10.2. Two levels designs with three factors.
10.3. Two levels designs for k factors.
10.4. Adding centerpoints in a general design at two levels.
10.5. Algorithm of Yates.
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