Descriptive Analytics/Estatística I: Inferência e Métodos Descritivos
Code
200035
Academic unit
NOVA Information Management School
Credits
7.5
Teacher in charge
Jorge Morais Mendes
Teaching language
Portuguese. If there are Erasmus students, classes will be taught in English
Objectives
This course covers techniques of multivariate statistical analysis. Students should be able, given a set of data and a particular goal, choose the appropriate methodology and have critical capacity in relation to the results obtained.
They should also have knowledge of the advantages, limitations and conditions for the use of various data analysis methods presented by discipline.
Prerequisites
Statistics and linear algebra (recomended)
Subject matter
1. Introduction to Multivariate Statistics Data Analysis
2. Fundamentals on data manipulation – introducing R software
3. Graphical representation of multivariate data
4. Multivariate normal distribution
5. Principal components analysis
6. (Exploratory) Factor Analysis
7. Cluster analysis
8. Correspondence analysis
9. Multidimensional scaling
10. Linear regression
11. Generalised linear models
12. time series data
Bibliography
Everitt, B. and Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R, Springer; Johnson, R.A and Winchern (2007), D. W., Applied Multivariate Statistical Analysis, 6th edition, Pearson Prentice Hall; Sharma, S., (1996) Applied Multivariate Techniques, John Wiley & Sons; Timm, N. H., (2002) Applied Multivariate Analysis, Springer; 0
Teaching method
The course is based on theoretical and practical classes. The classes are aimed at solving problems and exercises.
Evaluation method
- (60%) Final exam (1st or 2nd round dates)
- (40%) Project
Remarks:
1. A minimum grade of 9.5 points is required in final exam.