Teaching GuideTerm Faculty of Computer Science |
Mestrado Universitario en Técnicas Estadísticas (Plan 2019) |
Subjects |
Design and Analysis of Experiments |
Contents |
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Identifying Data | 2020/21 | |||||||||||||
Subject | Design and Analysis of Experiments | Code | 614493010 | |||||||||||
Study programme |
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Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
Official Master's Degree | 2nd four-month period |
First | Optional | 5 | ||||||||||
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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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