Multivariate Data Analysis – Instituto Politécnico de Coimbra

Base Knowledge

Teaching Methodologies

The development of each subject will be done in a similar way, in particular: an introduction and characterization of the technique followed by the application to a practical case using statistical software, with discussion of the options and the results.

Learning Results

It is intended that students understand and apply the multivariate data analysis techniques covered, that they use specific software properly and that they interpret critically the results obtained

Program

1. Introduction: Data type. Classification of multivariate techniques. Examples.

2. Principal Components Analysis: Definition, construction, properties, geometric meaning and interpretation. ACP on standardized data vs. non-standardized data. Analysis of practical cases using statistical software.

3. Factor Analysis: Formulation of the model. Estimation of loadings. Rotation of factors. Estimation of the values of the factors. Analysis of practical cases using statistical software.

4. Linear Regression Analysis: The model. Estimation of the coefficients. Inference on the model. Assumptions. Methods for sequential selection of variables. Analysis of practical cases using statistical software.

5. Cluster Analysis: dissimilarities between individuals. Hierarchical methods and nonhierarchical methods. Analysis of practical cases using statistical software.

6. Introduction to Structural Equation Models: general characterization.

Curricular Unit Teachers

Nuno Miguel Fortes Fonseca Santos

Grading Methods

Assessment by exam

- practical work 1 - 25.0%
- Exam - 50.0%
- practical work 2 - 25.0%

Periodic assessment

- test - 50.0%
- practical work 2 - 25.0%
- practical work 1 - 25.0%

Internship(s)

NAO

Bibliography

Hair, J. F., Black, W., Babin, B., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.).Pearson.

Johnson, R. A., & Wichern, D. W. (2008). Applied Multivariate Statistical Methods (6th ed.). Pearson.

Malhotra, N. (2012). Pesquisa de Marketing: Uma Orientação Aplicada (6.ª ed.). Bookman.

Marôco, J. (2021). Análise de Equações Estruturais – Fundamentos teóricos, software & aplicações (3.ªed.). ReportNumber.

Marôco, J. (2018). Análise Estatística com o SPSS Statistics (7.ª ed.). ReportNumber.

Pestana, M. H., & Gageiro, J. N. (2014). Análise de Dados para Ciências Sociais: A complementaridade do SPSS (6.ª ed.). Edições Sílabo.

Pestana, M. H., & Gageiro, J. N. (2005). Descobrindo a Regressão – com a complementaridade do SPSS (1.ªed.). Edições Sílabo.