Multivariate Data Analysis

Code:

PRODEC080

Acronym:

ADM

Keywords
Classification	Keyword
OFICIAL	Other

Instance: 2023/2024 - 1S

Active?	Yes
Responsible unit:	Department of Civil and Georesources Engineering
Course/CS Responsible:	Doctoral Program in Civil Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
PRODEC	7	Syllabus since 2007/08	1	-	5	45	135

Teaching language

Portuguese

Objectives

Provide knowledge on multivariate data analysis techniques.

Learning outcomes and competences

To give solid knowledge for future developments at the investigation or specialization level as well as at the professional level. To develop and to be able to use correctly multivariate data analysis methodologies, ensuring that a correct interiorization of concepts is achieved, as well as an appropriate discussion and interpretation of the outputs. To educate the student to be able to use correctly the statistic software of this knowledge area.  

Working method

Presencial

Program

Typology of variables. Multivariate methods for dimensionality reduction: factorial methods. Classification methods: hierarchical and non-hierarchical classification, discriminant analysis. Use of the adequate software to apply those methodologies.

DEMONSTRATION OF THE SYLLABUS COHERENCE WITH THE CURRICULAR UNIT'S OBJECTIVES.

Multivariate data analysis is an important tool in civil engineering research. The knowledge of the methodologies applicability, with resource the statistical software, and the correct interpretation of the results enables the achievement of the objectives.

Mandatory literature

Branco, J.A. (2004); “Uma Introdução à Análise de Clusters”, XII Congresso Anual da Sociedade Portuguesa de Estatística.
Gordon, A. (1999); “Classification” (2nd ed.), Chapman & Hall/CRC, Boca Raton.
Johnson, D.E. (1998); “Applied Multivariate Methods for Data Analysts”, Brooks/Cole, Pacific Grove.
Johnson, R. & Wichern, D. (1992).; “Applied Multivariate Statistical Analysis”, Englewood Cliffs: Prentice Hall International.
Maroco, J. (2007); “Análise Estatística com utilização do SPSS” (2.ª ed), Análise Estatística com utilização do SPSS.
Pestana, D.D & Velosa, S.F. (2002); “introdução à Probabilidade e à Estatística”, Volume 1, Fundação Calouste Gulbenkian, Lisboa. Saporta, G. (1990) “Probabilités Analyse des Données et Statistique”, Éditions Technip.

Teaching methods and learning activities

The teaching involves sessions with theoretical presentations and discussions. The student grading will be based on a report assignment.

DEMONSTRATION OF THE COHERENCE BETWEEN THE TEACHING METHODOLOGIES AND THE LEARNING OUTCOMES:

The report assignment and the discussion of the UC contents allows for an efficient learning experience at this education level

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Trabalho escrito	100,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	0,00
Total:	0,00

Eligibility for exams

Not applicable.

Calculation formula of final grade

.