Multivariate Analysis

Applied statistics

The classes are theoretical-practical in which the concepts and statistical methodologies are introduced and explained, followed by the
presentation of application examples. Activities involving the presentation, resolution and discussion of problems with multivariate data are
developed using statistical software.

In this curricular unit, it is intended to introduce the techniques of multivariate data analysis, paying attention to the methodologies as well as
its validation conditions and the interpretation of the results. It is also intended to provide students with specific technical skills that allow
them to implement solutions to different problems involving multiple variables and uncertainty.
At the end of the semester, students should be able to:
– perform analysis of variance and multivariate analysis of variance;
– perform factor analysis;
– perform cluster analysis;
– perform regression analysis (linear and logistic);
– use a statistical software to support multivariate data analysis.

1. Introduction to Multivariate Analysis
2. Analysis of Variance (ANOVA)
2.1 Parametric approach
2.2 Non-parametric approach
3. Multivariate Analysis of Variance (MANOVA)
3.1 Parametric approach
3.2 Non-parametric approach
4. Factor Analysis
5. Cluster Analysis
6. Extensions to Linear Regression
7. Logistic Regression

In this course, student will be assessed by the periodic assessment regime, which includes two mandatory components: an exam and a coursework with its oral defence. Therefore, assessment by exam alone is not possible.

 The exam is an individual written test, graded out of 10 points, with a minimum pass mark of 3 points. It is held during the teaching period, on a date to be announced by the teacher at least one week in advance. The content to be assessed will be communicated to students at least one week in advance.

 The coursework and its oral defence are graded out of 10 points, with a minimum pass mark of 5 points. The rules governing the use of generative artificial intelligence in the coursework will be properly specified at the time of its publication.

- If the coursework is submitted for assessment during the teaching period, it is a group coursework (2 to 3 students), with submission deadline and oral defence scheduled during that period.

- If the coursework is submitted for assessment during an examination period, it is an individual coursework, to be submitted no later than two working days before the date of the examination for which the student is registered, with the defence taking place on the examination date.

 The final grade (CF) is calculated using the following formula, with the result rounded to the nearest whole number: CF = CT + CF, where CT is the coursework grade, on a scale from 0 to 10 points, without rounding; and CF is the exam grade, on a scale from 0 to 10 points, without rounding.

Students must achieve a minimum final grade of 10 points to pass this course.

 Notes:

 1) A student who, in a given examination period, obtains the minimum grade in only one of the two assessment components will retain the grade for that component in subsequent examination periods within the same academic year until approval is obtained.

2) For grade improvement purposes, students may choose one of the following options:

a) take a new exam in the assessment period in which they are registered for grade improvement, while retaining the coursework grade;

b) take a new exam and complete new individual coursework, both in the assessment period in which the student is registered for grade improvement.

NAO