Statistical Methods and Applications

Learning goals The main goal of the course is to learn about common general computational tools and methodologies to perform reliable statistical analyses. Students will be able - to understand the theoretical foundations of the most important methods; - to appropriately implement and apply computational statistical procedures; - to interpret the results deriving from their applications to real data. Knowledge and understanding After attending the course, students will know and understand the most important computational techniques in statistical analysis. In addition, students will be able to appropriately implement the learned tools with the statistical software R and to develop original ideas often in a research context. Applying knowledge and understanding At the end of the course, students will be able to formalize statistical problems from a computational point of view, to apply the learned methods to solve them, also in contexts not covered in the lessons, and to interpret the results deriving from their applications to real data. Making judgements Students will develop critical skills through the application of computational methodologies to a wide range of statistical problems and through the comparison of alternative solutions to the same problem by using different tools. Furthermore, they will learn to interpret critically the results obtained by applying procedures to real datasets. Communication skills By studying and carrying out practical exercises, students will acquire the technical-scientific language of the discipline, which must be suitably used in the final written test. Communication skills will be also developed through group activities. Learning skills Students who pass the exam have learned computational techniques useful in the statistical analysis and to work self-sufficiently to face with the complexity of the statistical problems.