Multivariate Data Analysis

Code

400013

Academic unit

NOVA Information Management School

Credits

1.0

Teacher in charge

Weekly hours

2.0

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 understand underlying theory for the analysis of multivariate data developing ability to:

• Choose appropriate procedures for multivariate analysis
• Use R to carry out analyses
• Interpret the output of such analyses 
  

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.    Discriminant analysis
9.    Multidimensional scaling
10.    Repeated measures analysis

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

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

Courses

• Specialization in Central Banks Statistics
• Specialization in Official Statistics