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Multivariate Data Analysis

Multivariate Data Analysis

Code	13948
Year	3
Semester	S2
ECTS Credits	6
Workload	TP(60H)
Scientific area	Mathematics
Entry requirements	NA
Learning outcomes	It is intended that students be able to choose the appropriate methodology to handle multivariate statistical data and have critical capacity in relation to the results obtained, related to exploratory data analysis or linear or non-linear regression models. It is also intended that students use at least one statistical software in the analysis of these multivariate data (R, SPSS, etc.) At the end of this UC, it is expected that the student: - Know and apply different multivariate statistics methodologies; - Know and apply general inferential principles in multivariate models; - Be able to validate the assumptions of a multivariate statistical method; - Be able to estimate the parameters of a multivariate model; - Be able to interpret the parameters of a multivariate model; - Be able to present in both oral and written form results of a statistical analysis; - Be able to validate a multivariate model; - Be able to use statistical software.
Syllabus	1. Exploratory Analysis 1.1. Principal Component Analysis 1.2. Cluster Analysis 2. Modelling 2.1. Multiple Regression Analysis 2.2. Discriminant Analysis 2.3. Multinomial Regression 2.4. Multivariate Regression: Ridge, Lasso and PLS 2.5. Nonlinear Regression (nonlinear models; linearization; generalized least squares; maximum likelihood methods). 3. Model Validation 4. Applications to Sciences
Main Bibliography	F. J. Hair, W. C. Black, B .J. Babin, R. E. Anderson. “Multivariate Data Analysis, 7th Edition”. Pearson Education, 2010. B. S. Everitt, G. Dunn. “Applied Multivariate Data Analysis, 2nd Edition”. John Wiley & Sons, Ltd, 2001. B. Everitt, T. Hothorn. “An introduction to applied multivariate analysis with R”. Springer, 2011. R. Johnson, D. Wichern. “Applied Multivariate Statistical Analysis, 6th Edition”. Pearson, 2014. P. Mukhopadhyay. “Multivariate Statistical Analysis”. World Scientific, 2009. D. C. Montgomery, E. A. Peck, G. G. Vining. “Linear Regression Analysis”. John Wiley & Sons, 2001. R. Wehrens. “Chemometrics with R, Multivariate Data Analysis in the Natural Sciences and Life Sciences”. Springer, 2011. G. Casella, S. Fienberg, I. Olkin. “Modern Multivariate Statistical Techniques”. Springer, 2008. D. Zelterman. “Applied Multivariate Statistics with R”. Springer, 2015. S. Weisberg. “Applied Linear Regression”. John Wiley & Sons, 2014.
Language	Portuguese. Tutorial support is available in English.