Once successfully having studied this subject, the students should be able to: - Analize problems involving random phenomena - Define populations for a statistical study - Build Hypothesis about a distribution - Test hypothesis about the paramters of the chosen model - Evaluate how well does the model fit to reality - Understand the limitations of the methods that have been studied and the conditions under which they lead to wrong conclusions

CB1: Students have demonstrated possession and understanding of knowledge in an area of study that builds on the foundation of general secondary education, and is usually at a level that, while relying on advanced textbooks, also includes some aspects that involve knowledge from the cutting edge of their field of study. CE.FB1: Ability to solve mathematical problems that may arise in engineering. Ability to apply knowledge of: linear algebra; geometry; differential geometry; differential and integral calculus; differential and partial differential equations; numerical methods; numerical algorithms; statistics and optimisation. RA1: Have basic knowledge and understanding of mathematics, basic sciences, and engineering within the aerospace field, including: behaviour of structures; thermodynamic cycles and fluid mechanics; the air navigation system, air traffic, and coordination with other means of transport; aerodynamic forces; flight dynamics; materials for aerospace use; manufacturing processes; airport infrastructures and buildings. In addition to a specific knowledge and understanding of the specific aircraft and aero-engine technologies in each of the subjects included in this degree.

BLOCK 0: DESCRIPTIVE STATISTICS 0. Descriptive Statistics BLOCK I: PROBABILITY 1. Introduction to Probability 1.1 Introduction 1.2 Random phenomena 1.3 Definition of probability and properties 1.4 Assessment of probabilities in practice 1.5 Conditional probability 1.6 Bayes Theorem 2. Random variables 2.1 Definition of random variable 2.2 Discrete random variables 2.3 Continuous random variables 2.4 Characteristic features of a random variable 2.5 Transformations of random variables 2.6 Independence of random variables BLOCK II: PARAMETRIC MODELS AND INFERENCE 3. Distribution models 3.1 Binomial distribution 3.2 Geometric distribution 3.3 Poisson distribution 3.4 Uniform distribution (continuous) 3.5 Exponential distribution 3.6 Normal distribution (with CLT) 4. Statistical Inference 4.1 Introduction 4.2 Estimators and their distributions 4.3 Confidence Intervals 4.4 Hypothesis testing 4.5 Particualr tests on a single sample 4.6 Comparison of two populations BLOCK III: APPLICATIONS 5. Quality control 5.1 Introduction, control charts 5.2 Variables control charts, the X-bar chart 5.3 Attributes control charts, the p and np charts 6. Linear regression 6.1 Introduction 6.2 Simple linear regression 6.3 Multiple linear regression

- Lectures: introducing the theoretical concepts and developments with examples, 2.2 ECTS - Problem solving sessions: 2.2 ECTS - Computer (practical) sessions: 0.6 ECTS -- 4 SESSIONS - Evaluation sessions (continuous evaluation and final exam): 1 ECTS