Students should have completed our intro course ¿Mathematics for Social Sciences and Basic Statistics¿ as well as ¿Applied Quantitative Methods for the Social Sciences I. Based on these previous courses, students are expected to have a working knowledge of basic calculus, matrix algebra, probability theory, linear regression, and statistical computing using R.

The goal of this course is three-fold: (1) to prepare students to conduct research using appropriate statistical models and to communicate their results to a nontechnical audience; (2) provide a foundation in the theory of maximum likelihood so students can investigate and implement a wide range of statistical models; and (3) provide students with the tools necessary to learn more advanced statistical methods in the future. Knowledge or Content: K-7. Advanced knowledge and understanding of statistics applied to Social Sciences. K-8. Specialized and applied learning of quantitative research methods in the study of political and social phenomena. K-9. Advanced learning about the role of causality in Social Sciences. Skills: S-5. Ability to organize and express ideas clearly and unambiguously, and to support theoretical arguments on a topic through a critical analysis of the literature. S-7. Understanding the fundamental concepts of descriptive statistics, probability theory, and the foundations of inferential statistics. S-8. Knowing the properties of different types of quantitative data associated with the study of Social Sciences and mastering data analysis techniques. S-9. Understanding the techniques of causal inference in social research. Competences: C-7. Ability to select appropriate statistical models for data analysis within the framework of conducting research in Social Sciences. C-8. Handling quantitative research data: mastering analysis tools and data management software in the empirical development of a research paper. C-9. Being able to generate new data and apply causal inference techniques in the empirical development of a research question.

1. Multiple regression with stochastic regressors 1.1 Revision of basic concepts 1.2 Distributions of the OLS estimators 1.3 Inference 1.4 Confidence Intervals 2. Models with categorical dependent variables. 2.1 The linear probability model. 2.2 The probit, logit models and interpretation. 2.3 Estimation and inference in models with categorical dependent variables. 3. Other qualitative dependent variable models. 3.1 Multinomial models. 3.2 Estimation and inference. 4. Models with count data. 4.1 Estimation and inference. 5. Models with Panel Data: static models and control for unobserved heterogeneity. 5.1 Fixed effects models. 5.2 Random effects models . 5.2 Tests for model selection.

Training Activities: AF3 - Theoretical-practical class: learning theoretical content on mathematics, statistics, and causal inference. AF4 - Laboratory practices: using software programs on computers to develop statistical models learned in theoretical classes. AF5 - Tutorials: the possibility of weekly meetings with the course instructor. AF6 - Individual student work. Teaching Methodologies: MD1 - Classroom presentations by the professor with the support of computer and audiovisual media, in which the main concepts of the subject are developed. MD3 - Resolution of practical cases, problems, etc., proposed by the professor individually or in groups. MD5 - Preparation of individual or group assignments and reports.