Statistical data analysis

The classes are, in accordance with the curriculum, theoretical-practical, planned and prepared according to active methodologies, to
ensure the active involvement of all students at various points during the class or throughout the entire class.
In the theoretical part, which introduces concepts, fundamental results and methods, the expository method will tend to be used,
interspersed with tasks that encourage the active participation of all students. These tasks include asking questions to and by students,
orally, and also proposing discussion in small groups on some aspect/topic presented.
The practical part will be devoted to the full development of the listed skills, through the commented exemplification of procedures and/or
problem solving under the guidance/tutoring of the teacher, encouraging independent work or work in small groups. There will be a strong
interaction between theory and practice, with a central role for the visualisation and treatment of concrete and real situations.

Statistics is a science that plays a central role in data analysis and the construction of decision support systems, providing essential
methodologies for transforming information into knowledge. The use of statistical techniques is fundamental for modelling, forecasting and
evaluating complex scenarios, supporting evidence-based decisions in multiple organisational domains. This course will cover inferential
statistical methods and analysis models applied to real data sets.
The following learning objectives are therefore defined:
1. understand and apply fundamental statistical concepts in the context of decision-oriented analysis;
2. identify, select and apply statistical models appropriate for solving real problems;
3. design and develop complex statistical studies, integrating advanced data analysis techniques;
4. use software to support the implementation of statistical techniques.

1. Introduction
1.1. Review of basic statistical concepts
1.2. Parametric and non-parametric hypothesis tests
2. Statistical models
2.1. Linear regression model
2.2. Models with discrete dependent variables

NAO