SPECIFICS OBJECTIVES: 1. Learn to organize, synthesize, and analyze univariate and bivariate data. 2. Understand and interpret published statistical studies. 3. Formulate and solve basic probability problems. 4. Perform basic statistical analyses with the help of software. TRANSVERSE OBJECTIVES: 1. Capacity of analysis and synthesis. 2. Knowing how to use statistical software. 3. Problem solving. 4. Teamwork. 5. Critical reasoning. 6. Verbal and written communication.

Students are able to¿ 3.1 ¿understand and describe theories and approaches related to urbanism and urbanisation. 4.1 ¿review and describe the underlying concepts, principles, academic literature and contemporary issues associated with urban sustainability. 5.1 ¿review and describe environmental, social and economic systems, identifying relationships between them. 6.1 ¿evaluate the appropriateness of different approaches to solving problems, analysing data and drawing sound conclusions in accordance with basic theories and concepts. 8 ¿consider academic norms and ways of thinking across different disciplines and subject areas, bringing them into play as appropriate. 9 ¿demonstrate intellectual curiosity, critical thinking, and exercise independence of mind and thought. 10 ¿communicate ideas clearly, coherently and respectfully, in a range of disciplines and to various stakeholders, in both written and oral form, using appropriate language and referencing. 12 ¿work independently, meet deadlines, manage their own time and workload and demonstrate initiative. 13 ¿reflect on their own learning, to seek and make use of feedback on their own performance, to recognise when further knowledge is required and to undertake the necessary research.

1. Introduction. 1.1. Concept and uses of statistics. 1.2. Statistical terminology: populations, subpopulations, individuals and samples. 1.3. Types of variable. 2. Analysis of univariate data. 2.1. Representations and graphs of qualitative data. 2.2. Representations and graphs of quantitative data. 2.3. Numerical summary. 3. Analysis of bivariate data. 3.1. Representations and graphs of qualitative and discrete data. 3.2. Representations and graphs of continuous data: correlation and regression. 4. Probability and probability models. 4.1. Random experiment, simple 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. Ideas and objectives. 5.2. Point estimation. 5.3. Interval estimation. 5.4. Fundamental concepts of hypothesis tests. 5.5. Tests for the mean in normal populations. 5.6. Tests for proportions.

THEORY (3 ECTS): Lectures will be delivered, with supporting materials available on the Aula Global platform, to help students acquire the necessary competencies. PRACTICAL (3 ECTS): Classes focused on solving exercises and problems. Computational practices using statistical software. Oral presentations.