Students will acquire knowledge and skills necessary to: 1. Define populations for a statistical study 2. Compute point estimators and confidence intervals for population parameters 3. Build Hypothesis about a distribution 4. Test hypothesis about the parameters of the chosen model 5. Evaluate how well does the model fit to reality 6. Understand the limitations of the methods that have been studied and the conditions under which they lead to wrong conclusions 7. Carry out the abovementioned analyses in statistical software Students will be able to: 1. Develop their ability to think analytically 1. Become familiar with a statistical software 2. Establish a framework to solve problems 3. Develop their interactive skills 4. Enhance their critical thinking 5. Improve their learning skills and communication

K3: To know fundamental contents in their area of study starting from the basis of general secondary education and reaching a level proper of advanced textbooks, including also some aspects of the forefront of their field of study. K4: Knowledge of basic scientific and technical subjects that qualify for the learning of new methods and technologies, as well as providing a great versatility to adapt to new situations, in the field of data storage, management and processing. K5: Ability to understand and relate fundamental concepts of probability and statistics and be able to represent and manipulate data to extract meaningful information from them S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S5: Ability to correctly identify predictive problems corresponding to certain objectives and data, based on knowledge of algorithms, modeling, prediction and filtering, and to use the basic results of regression analysis as the basis for prediction methods S16: Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally to both specialized and non-specialized audiences C5: Be able to analyze and synthesize basic problems related to engineering and data science, elaborate, defend and efficiently communicate solutions individually and professionally, applying the knowledge, skills, tools and strategies acquired or developed in their area of study.

1. Introduction to Statistical inference. 1.1. Population and sample 1.2. Random sampling 1.3. Fundamental sampling distributions 1.4. Point estimation of parameters 1.4.1. Definitions 1.4.2. Method of moments 1.4.3. Maximum likelihood estimation 2. Confidence intervals for a single sample 2.1. Introduction 2.2. CI on the mean 2.2.1. Normal population with known variance 2.2.2. Large sample 2.2.3. Normal population unknown variance 2.3. CI on the proportion 2.3.1. Large-sample 2.4. CI on the variance 2.4.1. Normal population 3. Test of hypotheses for a single sample 3.1. Introduction 3.2. Type I and Type II Errors 3.3. Power of a statistical test 3.4. P-value 3.5. HT on the mean 3.5.1. Normal population with known variance 3.5.2. Large sample 3.5.3. Normal population with unknown variance 3.6. HT on the proportion 3.6.1. Large sample 3.7. HT on the variance 3.7.1. Normal population 4. Statistical inference for two samples 4.1. Introduction 4.2. Difference in means 4.2.1. Normal populations with known variances 4.2.2. Large samples 4.2.3. Normal populations with unknown variances 4.2.4. Normal populations with unknown equal variances 4.2.5. Normal populations, paired observations. 4.3. Difference in proportions 4.3.1. Large samples 4.4. Ratio of the variances 4.4.1. Normal populations 5. Analysis of Variance 5.1. Introduction 5.2. One-way ANOVA 5.3. ANOVA table 5.4. Multiple comparisons 5.5. Two-way ANOVA. 6. Goodness of fit tests 6.1. Introduction 6.2. Chi-square tests 6.3. Kolmogorov-Smirnov test 6.4. Graphical tools

- 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