2025
Multivariate Data Analysis
Name: Multivariate Data Analysis
Code: MAT02557L
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Mathematics
Teaching languages: Portuguese
Languages of tutoring support: Portuguese
Regime de Frequência: Presencial
Sustainable Development Goals
Learning Goals
Objectives:
? Select the most appropriate statistical tool for the type of multivariate analysis under consideration.
? Interpret the results obtained using statistical software.
Competencies:
? Perform analyses and association measures from a multivariate treatment perspective.
? Understand techniques of factor analysis (simple and multiple).
? Comprehend the basic algorithms of simple and multiple multivariate analyses.
? Use the main statistical processing software.
? Communicate the results of multivariate statistical analyses and their interpretation.
? Select the most appropriate statistical tool for the type of multivariate analysis under consideration.
? Interpret the results obtained using statistical software.
Competencies:
? Perform analyses and association measures from a multivariate treatment perspective.
? Understand techniques of factor analysis (simple and multiple).
? Comprehend the basic algorithms of simple and multiple multivariate analyses.
? Use the main statistical processing software.
? Communicate the results of multivariate statistical analyses and their interpretation.
Contents
1. Complements and Reviews of Fundamental Statistics Concepts.
2. Univariate analysis of variance. One-factor variance analysis. Two-factor analysis of variance. Non-parametric analysis of variance. Repeated measures ANOVA.
3. Multivariate Analysis of Variance (MANOVA). One-way MANOVA. Two-factor MANOVA.
4. Principal Component Analysis (PCA). The Principal Components Model. Use of Main Components.
5. Factor Analysis (FA). The Factor Analysis Model. Comparison between ACP and AF.
6. Classification Analysis (CA). Measures of similarity and dissimilarity. Hierarchical grouping of groups (clusters). Classification Analysis with variables.
7. Introduction to linear regression analysis. The Model and Assumptions.
2. Univariate analysis of variance. One-factor variance analysis. Two-factor analysis of variance. Non-parametric analysis of variance. Repeated measures ANOVA.
3. Multivariate Analysis of Variance (MANOVA). One-way MANOVA. Two-factor MANOVA.
4. Principal Component Analysis (PCA). The Principal Components Model. Use of Main Components.
5. Factor Analysis (FA). The Factor Analysis Model. Comparison between ACP and AF.
6. Classification Analysis (CA). Measures of similarity and dissimilarity. Hierarchical grouping of groups (clusters). Classification Analysis with variables.
7. Introduction to linear regression analysis. The Model and Assumptions.
Teaching Methods
* Theoretical and laboratory classes mainly lectured with a blackboard, with e-learning tools and transparencies. Laboratory practices using the statistical software used.
* Introduction of the theoretical concepts using practical examples and trying to show the relevance of the contents in the main area of application.
Exercises directed to the area of the course in question.
* Encourage individual and group participation in the classroom with the discussion of examples and exercises aimed at the area of Human and Social Sciences, focusing on solving current and real problems.
* Introduction of the theoretical concepts using practical examples and trying to show the relevance of the contents in the main area of application.
Exercises directed to the area of the course in question.
* Encourage individual and group participation in the classroom with the discussion of examples and exercises aimed at the area of Human and Social Sciences, focusing on solving current and real problems.
Assessment
Continuous evaluation
? The continuous assessment regime consists of 2 tests.
? In each test, the student must have a grade greater than or equal to 8 points.
? The final grade will be the arithmetic average of the grades obtained in the two tests.
? To obtain approval for the subject, the final grade must be equal to or greater than 9.5 and you must have attended at least 75% of the classes.
Final Assessment
? The final grade in the Normal Exam assessment system must be equal to or greater than 9.5 and the student must have attended at least 75% of the classes to obtain approval for the curricular unit.
? The material for assessment will be all that taught during the semester.
Appeal Exam
? If the student has not passed, he or she may take an Appeal Exam
If the final classification obtained in the subject is higher than 16 points, the teacher may require an oral test.
? The continuous assessment regime consists of 2 tests.
? In each test, the student must have a grade greater than or equal to 8 points.
? The final grade will be the arithmetic average of the grades obtained in the two tests.
? To obtain approval for the subject, the final grade must be equal to or greater than 9.5 and you must have attended at least 75% of the classes.
Final Assessment
? The final grade in the Normal Exam assessment system must be equal to or greater than 9.5 and the student must have attended at least 75% of the classes to obtain approval for the curricular unit.
? The material for assessment will be all that taught during the semester.
Appeal Exam
? If the student has not passed, he or she may take an Appeal Exam
If the final classification obtained in the subject is higher than 16 points, the teacher may require an oral test.
Teaching Staff
- Maria Manuela Melo Oliveira [responsible]
Certificações
