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. Exploratory Data Analysis. 1.1. Introduction. 1.2. Types of Variables. 1.3. Univariate Data Analysis. 1.4. Bivariate Data Analysis. 2. Statistical Inference. 2.1. Introduction. 2.2. Point and Interval Estimation. 2.3. Hypothesis Testing of Means and Proportions. 2.4. ANOVA Test. 3. Relationships Between Variables. 3.1. Introduction. 3.2. Relationships Between Qualitative Variables. Chi-Square Statistic. 3.3. Relationships Between Quantitative Variables. Correlation. 4. The Simple Regression Model. 4.1. Purpose of Analysis. 4.2. Estimation and Interpretation. 4.3. Diagnosis and Goodness of Fit. 4.4. Prediction.

Theory (3 ECTS): During theoretical sessions, the contents of the course will be introduced, explained, and illustrated with examples. Teaching materials will be provided on the Internet. Practice (3 ECTS): Blackboard exercises will be solved during practical sessions. Optionally, these activities can be complemented with applications to real datasets using statistical software. It is highly recommended that group review sessions be organized in the weeks before the midterm and final exams to address questions related to those exams.