Statistical inference I Statistical inference II Regression methods

-Being able to identify and propose the correct model for a specific problem -Ability to manage computationally and analiticaly the models proposed and carry out the analysis of the resuts obtained. -Ability to model and analyze static and dynamic data -Ability to validate models and interpret the results -Ability to draw conclusions and write reports -Ability to work in multidisciplinar groups

K2: Know basic humanistic contents, oral and written expression, following ethical principles and completing a multidisciplinary training profile. K4: Know the models and methods of statistical analysis for both static and dynamic data K12: now how to identify or be able to create the statistical or probabilistic model appropriate to the specific problem arising in each business activity (finance, marketing, production planning and control, etc.). C1: Develop and master interpersonal skills on initiative, responsibility, conflict resolution and negotiation, which are essential in the professional environment. C4: Ability to develop and validate statistical models that help to address and solve problems relevant to today's society. C7: Ability to access, analyze and classify large volumes of data of a highly heterogeneous nature (Big Data), as well as to manage and design advanced data analysis and AI tools in social and business applications. S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S5: Manipulate computationally and analytically the established models, taking advantage of the power of statistical methods, optimization, etc., and perform the analysis of the results obtained.

1. Revision of linear models 1.2 Estimation 1.3 Inference 2. Introduction to generalized linear models 2.1 Exponential family 2.2 Components of a GLM 2.3 Estimation: Fisher Scoring Algorithm 2.4 Inference 2.5 Diagnostics 3. Models for binary data and proportions 3.1 Logistic regression 3.2 Parameter interpretation: Odds ratio 3.3 Validation: ROC curve 4. Models for count data 4.1 Poisson regression 4.2 Log-linear models 5. Generalized additive models 5.1 Smoothing techniques 5.2 Estimation and inference 6. Random effects models 6.1 Estimation 6.2 Inference 6.3 Models for repeated measures and longitudinal data