Statistics for Social Sciences I or a similar introductory statistics course.

Specific competences: 1. Understanding the basic concepts of statistical multivariate analysis and its applications in the social sciences. 2. Capacity for applying simple linear regression and interpreting the results. 3. Capacity for applying multiple linear regression and interpreting the results. 4. Capacity for applying binomial logistic regression and interpreting the results. 5. Capacity for applying principal component analysis and interpreting the results. 5. Capacity for applying cluster analysis and interpreting the results. 7. Effective use of statistical software. Transversal competences: 1. Capacity for analysis and synthesis. 2. Capacity for mathematical and statistical modeling. 3. Problem solving. 4. Critical reasoning. 5. Oral and written communication.

Topic 1. Linear regression. 1.1. Linear regression. Introduction; simple and multiple regression; motivation; graphical data analysis; model formulation; dummy variables; parameter interpretation; examples; applications. 1.2. Fitting the model to the data; the least squares criterion; using the fitted model. 1.3. Model assumptions; inference on model parameters I: confidence intervals; inference on the response. 1.4. Inference on model parameters II: hypothesis testing; statistical significance of estimated parameters. 1.5. Assessing model fit; ANOVA. 1.6. Selection of predictor variables; multicollinearity; model diagnostics; model validation. Topic 2. Binomial logistic regression. 2.1. Motivation; model assumptions and formulation; parameter interpretation; examples; applications. 2.2. Fitting the model to the data; using the fitted model; inference on model parameters; statistical significance of estimated parameters. 2.3. Assessing model fit; selection of predictor variables; multicollinearity. Topic 3. Principal component analysis. 3.1. Motivation; formulation; variance explained; examples; applications. 3.2. Deciding the number of components to keep; component scores; interpretation of components; graphical representations. Topic 4. Cluster analysis. 4.1. Motivation; examples; applications; agglomerative hierarchical methods. 4.2. Graphical representations; dendrograms; similarity measures. 4.3. Fit; interpretation of clusters; more application examples.

Theory (3 ECTS). Theory classes with supporting material available in the course's web page. Practical classes (3 ECTS). Problem-solving classes. Practical classes in computer rooms. Weekly individual tutoring sessions. The teaching methodology will be eminently practical, being based on the study of diverse data sets through multivariante analysis techniques, both in the theory and practical classes, as motivation and illustration of the theory.