Base Knowledge
Univariate and bivariate descriptive statistics.
Teaching Methodologies
The development of each topic in the course will have a similar structure, in particular: an introduction and characterization of the technique, with presentation and discussion of the options and outputs of SPSS using an example, and practical case resolution.
Learning Results
The main goal of this course is to provide students with a set of techniques for data analysis when they involve more than two variables. In particular, it is intended that students deepen and broaden the knowledge they need to analyze data, understand the aim of the covered techniques, apply them using an appropriate software (SPSS) and correctly interpret the results.
Program
Introduction: Data type. Classification of multivariate techniques. Examples.
Factor Analysis: the model. Estimation of loadings. Rotation of factors. Estimation of the values of the factors. Resolution of application examples using SPSS and interpretation of the outputs.
Principal Components Analysis: definition, construction, properties, geometric meaning and interpretation. ACP on standardized data vs. non-standardized data. Resolution of application examples using SPSS and interpretation of the outputs.
Multiple Linear Regression Analysis: the model; estimation of coefficients; inference on the model. Assumptions. Methods of sequential selection of variables. Resolution of applied examples using SPSS and interpretation of results.
Cluster Analysis: dissimilarities between individuals. Hierarchical methods and nonhierarchical methods. Resolution of application examples using SPSS and interpretation of the outputs.
Curricular Unit Teachers
Grading Methods
- - Trabalho prático 1 - 25.0%
- - Teste - 50.0%
- - Trabalho prático 2 - 25.0%
- - Trabalho prático 2 - 25.0%
- - Trabalho prático 1 - 25.0%
- - Teste - 50.0%
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. (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.ª ed.). Edições Sílabo.
Wooldridge, J.M. (2020). Introductory Econometrics: A Modern Approach (7.ª ed.). Cengage Learning.