Probability Statistical Inference I Statistical Inference II

General objectives: 1. Capacity for analysis and synthesis. 2. To model and solve problems. 3. Oral and written communication skills. Specific objectives: 1. Knowledge, both in theory and practice, of the foundations of the main techniques of survey sampling. 2. Differentiation of the different types of sampling. 3. Ability to make inference in finite populations under complex sampling designs.

K2: Know basic humanistic contents, oral and written expression, following ethical principles and completing a multidisciplinary training profile. K6: Demonstrate basic programming skills in order to use and develop statistical packages K11: Know the methodology that allows to statistically describe a set of data from numerical measures and graphs, both univariate and multivariate, highlighting possible relationships between variables of interest. K13: Know the concepts necessary for the development of market research as well as the main tools to analyze the results of market research 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. C6: Ability to interpret the results of quantitative analysis, prepare clear reports and communicate conclusions effectively, using advanced data analysis tools. S6: Communicate the results, the conclusions of the models and the proposed solutions in a way that is intelligible to the rest of the company, so that they are accepted and implemented by the decision-makers S7: Describe, synthesize and graphically represent the information contained in a data set

1. Introduction and general considerations about sampling techniques and Official Statistics - 1.1. Importance of Official Statistics and its role in Society - 1.2. Sampling Basics - 1.3. Types of sampling: probabilistic vs. non-probabilistic - 1.4. Introduction to the statistical software R and its importance in Official Statistics 2. Probability Sampling - 2.1. Definition and principles of probability sampling - 2.2. Advantages and limitations of probability sampling - 23. Implementation in R: basic functions for probability sampling 3. Simple random sampling - 3.1. Concepts and theory of simple random sampling - 3.2. Procedures for performing simple random sampling - 3.3. Examples of simple random sampling in different contexts - 3.4. Using R to perform simple random sampling: specific functions and packages 4. Stratified random sampling - 4.1. Fundamentals of Stratified Random Sampling - 4.2. Criteria for stratification and selection of strata - 4.3. Advantages of stratified sampling over other techniques - 4.4. Running Stratified Sampling in R: Recommended Code and Packages 5. Ratio and Regression Estimators - 5.1. Introduction to ratio and regression estimators - 5.2. Applications of ratio and regression estimators in research - 5.3. How to calculate ratio and regression estimators in R - 5.4. Practical examples: use of estimators for data analysis 6. Cluster Sampling - 6.1. Basic concepts of cluster sampling - 6.2. Design and analysis of cluster samples - 6.3. Comparison with other sampling techniques - 6.4. Implementation of cluster sampling in R

Competences will be acquired by students both through theoretical lectures and the resolution of assigned homeworks. There will also be practical classes of exercises.