Linear algebra Calculus Programming

By the end of this course, students will be able to have: 1. Knowledge and understanding of the statistic principles underlying their branch of engineering 2. The ability to apply their knowledge and understanding to identify, formulate and solve statistic problems using established methods 3. The ability to apply their knowledge and understanding to analyse engineering products, processes and methods 4. An understanding of statistics methodologies, and an ability to use them 5. The ability to select and use appropriate statistic tools and methods 6. The ability to combine theory and practice to solve engineering problems 7. An understanding of applicable statistic techniques and methods, and of their limitations

1. Descriptive statistics 1.1. Qualitative data vs quantitative data 1.2. Descriptive statistics for one variable 1.3. Descriptive statistics for two variables 2. Probability 2.1. Introduction to probability 2.2. Events and operations with events 2.3. Definition and properties of probability 2.4. Conditional probability and independence 2.5. The law of total probability 2.6. Bayes' theorem 3. Random variables 3.1. Concept of random variable 3.2. Discrete random variables 3.3. Continuous random variables 3.4. Characteristic measures of a random Variable 3.5. Independence of random variables 4. Distribution models 4.1. Binomial 4.2. Geometric 4.3. Poisson 4.4. Uniform (continuous) 4.5. Exponential 4.6. Normal (CLT) 5. Statistical inference 5.1. Introduction 5.2. Estimators and their sampling distributions 5.3. Confidence intervals 5.4. Hypothesis testing 6. Quality control 6.1. Introduction to quality control 6.2. Control charts for variables 6.3. Control charts for attributes 7. Linear regression 7.1. Introduction 7.2. Simple linear regression 7.3. Multiple linear regression

- Lectures: 2,2 ECTS - Problem solving sessions: 1,8 ECTS - Computes sessions: 1 ECTS - Evaluation sessions (continuous evaluation and final exam): 1 ECTS