Statistical data analysis

CB1: Probability

CB2: Inferential Statistics

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.
ME1. 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.
ME2. 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:
OA1. understand and apply fundamental statistical concepts in the context of decision-oriented analysis;
OA2. identify, select and apply statistical models appropriate for solving real problems;
OA3. design and develop complex statistical studies, integrating advanced data analysis techniques;
OA4. use software to support the implementation of statistical techniques.

Upon successful completion of this course, students will have developed the following competences:

CD1.Apply advanced statistical methodologies to analyse data and support evidence-based decision-making by critically selecting appropriate models and rigorously interpreting their results.

CD2. Design, implement and validate statistical studies using specialised statistical software, integrating inferential techniques and analytical models to address complex organisational problems.

P1. Introduction
1.1. Review of basic statistical concepts
1.2. Parametric and non-parametric hypothesis tests
P2. Statistical models
2.1. Linear regression model
2.2. Models with discrete dependent variables

In accordance with the Academic Regulation of the 1st study cycle of IPC, the following assessment system is proposed

A) Periodic Evaluation

The evaluation of this subject is done through a practical work (mandatory) carried out in a group, with a maximum of 4 students, (NT), and a final exam, (NE).

The final grade (CF) is given by CF = 0.6*NE + 0.4*NT.

The minimum score for the exams is 5 points.

This evaluation is valid for all evaluation seasons (normal, appeal and special).

If students do not submit their work in the normal period, they will have to do so in one of the following evaluation periods.

The grade of the work delivered on the normal exam date is valid for all evaluation periods.

For approval in the course it is necessary to obtain a final mark of 10 or more.

The practical work will be evaluated and discussed throughout classes.

Notes:

1) The use of generative AI is recommended as a study aid, helping to improve understanding of the course content. However, exam answers must follow the methods taught in class; answers that use alternative methods will not be accepted.

2) In the event of a well-founded suspicion of cheating detected during the marking of the exam, the student will be summoned to sit an oral examination to verify the knowledge and skills demonstrated. This assessment is carried out by an examination board comprising the course unit lecturer and two further lecturers from the same subject area. If the student is unable to demonstrate the knowledge and competences reflected in the written examination, the examination will be declared void.

NAO