Statistics Applied to Information Systems

The classes are designed, according to the curricular plan, to be both theoretical and practical. They are planned and prepared considering
active learning activities, to actively engage all students at various moments or throughout the entire class.
In the theoretical part of the lesson, which introduces concepts, fundamental results, and methods, the expository method will predominantly
be used, interspersed with tasks that encourage active participation from all students. These tasks include posing questions to and by
students, orally, as well as proposing debates/discussions in small groups on certain exposed aspects/topics.
The practical part will be designed to comprehensively develop the listed skills. This will be achieved through problem-solving, using
software, under the guidance of the teacher. Autonomous work or work in small groups will be encouraged. There will be a strong
interaction between theory and practice, with a central focus on visualizing and dealing with actual scenarios.

Statistics is a science of recognized importance, with applications in various scientific domains, including the field of information systems.
Thus, this course aims to introduce a set of statistical techniques for processing, analyzing, and interpreting data within the context of
information systems, using statistical software.
The following learning outcomes are therefore defined:
1. Plan the stages of the statistical method, specifically identifying the problem, processing the data, and selecting the most appropriate
statistical techniques according to the defined objectives;
2. Carry out statistical analyses using software, extracting the relevant and essential information from the output;
3. Interpret the results of the statistical analysis, determining to what extent they address or clarify the established objectives.

1. Basic Concepts
1.1 Statistics and Machine Learning
1.2 Statistical techniques for descriptive analysis
1.3 Hypothesis Testing
2. Multivariate Statistics
2.1 Factor analysis
2.2 Clusters analysis
3. Econometric Models
3.1 Regression models
3.2 Extension of the regression models

NAO