Specific competences: 1. Understand the basic concepts of population, sample, variable and statistic. 2. Know how to summarize a sample using measures of centre and variability. 3. Learn how to use statistical graphs to illustrate the main features of a sample. 4. Understand and implement the basic ideas of a regression analysis. 5. Learn how to estimate a population parameter based on sample data and how to formalize a hypothesis test. 6. Use of statistical software. Transversal competences: 1. Capacity of analysis and synthesis. 2. Understanding of how to use computer packages. 3. Problem solving. 4. Teamwork. 5. Critical reasoning. 6. Verbal and written communication.

1. Introduction. 1.1. Concept and uses of statistics. 1.2. Statistical terminology. 1.3. Typos of variables. 2. Analysis of univariate data. 2.1. Representations and plots of qualitative data. 2.2. Representations and plots of quantitative data. 2.3. Numerical summary of a sample of data. 3. Analysis of bivariate data. 3.1. Representations and plots of qualitative and discrete data. 3.2. Representations and numerical summaries of quantitative data: correlation and regression. 3.3. Time series. 4. Probability and probabilistic models. 4.1. Random experiments, sample space, elementary and composite events. 4.2. Properties of probability. 4.3. Conditional probability and its properties. 4.4. Random variables and their characteristics. 4.5. Bernoulli trials and related distributions. 4.6. The normal distribution. 5. Introduction to statistical inference. 5.1. Outline and objectives. 5.2. Point estimators. 5.3. Interval estimators. 5.4. Fundamentals of hypothesis testing. 5.5. Tests for normal means. 5.6. Tests for proportions.

Theory (4ECTS). Theory classes with materials available on the web. Prácticas (2ECTS) Problem classes. Computing classes using statistical software. Group tutorials for resolution of problems, doubts etc.