In this course are introduced the basic concepts of multivariate analysis as an extension of univariate statistical analysis. Exploratory methods are discussed, classification and analysis of the interdependence of nature multivariate data. Study of some examples with software.
Multivariate data and multivariate tests Principal components analysis Clustering techniques Regression analysis
Upon completion of this learning unit, the student should be able to: - identify, characterize and distinguish at the deeper level the various multivariate techniques of the program; - select and apply a methodology to a multivariate data set; - interpret results identify limitations; - apply comfortably statistical software SPSS or one that will be adopted.
1. Introduction to multivariate data
2. Multivariate tests. Multivariate analysis of variance - MANOVA
3. Principal Component Analysis and Factor Analysis
4. Discriminant Analysis
5. Cluster Analysis
6. Topics of Regression Analysis
- Reis, E. (2001) Estatística Multivariada Aplicada, 2ª Edição, Edições Sílabo, Lisboa.
- Marôco, J. (2018) Análise Estatística com o SPSS Statistics, ReportNumber.
- Jonhson, R. A., Wichern D. W. (2007) Applied Multivariate Statistical Analysis, Pearson Prentice Hall.
- Manly, B.F.J. (2005) Multivariate Statistical Methods, Chapman & Hall /CRC.
- Hair, JF, et al. (2014) Multivariate Data Analysis, 7th Edition, Pearson Education Limited.
Evaluation is made on individual basis and it involves the coexistence of two modes: continuous assessment (60%) and
final evaluation (40%). Further information is detailed in the Learning Agreement of the course unit.
Are prerequisites for the monitoring of UC knowledge of Linear Algebra, Descriptive Statistics and Statistical Inference. The Universidade Aberta provides a license to use the software IBM SPSS at no additional cost to the student, if this is the software chosen. Other software will be Open Access.