Data Analysis
Code
100003
Academic unit
NOVA Information Management School
Credits
6.0
Teacher in charge
Weekly hours
45.0
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 be able, given a set of data and a particular purpose, choose the appropriate methodology and have critical capacity regarding the results obtained.
They should also have knowledge of the advantages, limitations and conditions of applicability of various data analysis methodologies presented by discipline.
Prerequisites
Statistics and linear algrebra (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, Wiley
Reis, E. (1997), Estatística Multivariada Aplicada, Edições Sílabo
Reis, E. (1997), Estatística Multivariada Aplicada, Edições Sílabo
Teaching method
The course is based on theoretical and practical classes. The classes are aimed at solving problems and exercises.
Evaluation method
- 1st Round: 3 Tests (70%) + 3 Homework assignments (30%)
- 2nd Round: Final exam (70%) + 3 Homework assignments (30%)
Remarks:
1. A single minimum grade of 7.5 points and an average of 9.5 of the two tests are required; otherwise you are only qualified fo second round assessment.
2. A minimum grade of 9.5 points is required in final exam at 2nd round; otherwise you do not get approved.