Statistical Inference Techniques I Statistical Inference Techniques II Regression Methods

SPECIFIC SKILLS 1. Understanding the basic techniques for analyzing categorical data. 2. Knowing and managing statistical programs for the analysis of categorical data. 3. Using the methodology for the analysis of real data. TRANSVERSAL COMPETENCES: 1. Capacity for analysis and synthesis. 2. Modeling and resolution of problems. 3. Oral and written communication.

1. Introduction. 1.1. Categorical Response Data. 1.2. General approach to different statistical techniques. 1.3. Examples. 2. Contingency Tables. Measures of relationship and association. Contrasts. 2.1. Association Measures for categorical data. 2.2. Statistical inference (parametric and nonparametric). 2.3. Examples. 3. Simple and multiple correspondence analysis. 3.1. Introduction: assumptions, estimation and interpretation. 3.2. Simple correspondence analysis. 3.3. Multiple correspondence analysis. 3.4. Examples. 4. Decision trees. 4.1. Introduction: assumptions, estimation and interpretation. 4.2. Algorithms: CHAID, CART and QUEST. 4.3. Examples. 5. Generalized Linear Models (GLM). Models for binary data (logistic regression) and multiple response. 5.1. Introduction to GLM and comparison with other models. 5.2. Limited dependent variable models: models for binary data. Binary logistic regression: assumptions, estimation and interpretation. 5.3. Models for multinomial data. Multiple Logistic Regression: assumptions, fitting and interpretation. 5.4. Examples.

Theory (4 ECTS). Theoretical classes with support material available on the Web. Practice (2 ECTS) problem-solving classes. Computing practices in computer lab.