NOVA Information Management School

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.    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; 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.

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