Learning objectives
The main goal of the course is to supply some fundamental topics of multivariate analysis. Goal of the course is also to teach to the student to use multivariate analysis in the solution of applied problems which have a particular relevance in the firm.
Prerequisites
Knowledge of the basics of Statistics.
Course unit content
1. Means.
2. Variability indexes.
3. Centroids.
4. Outliers.
5. Variance Covariance Matrix.
6. Variable transformations.
7. Multivariate distances.
8. Discriminant Models.
9. Principal Components.
10. Multivariate regression.
Full programme
1. Means and their properties.
2. Variability indexes.
3. Centroids at two and more dimensions.
4. Outliers.
5. Variance Covariance Matrix and their properties.
6. Main random variables.
7. Variable transformations.
8. Multivariate distances.
8.1 Minkowski Metric.
8.2 Mahalanobis Metric.
9. Discriminant Models.
9.1 Fisher Discriminant Analysis.
9.2 Likelihood Discriminant Analysis.
10. Principal Components.
11. Regression.
11.1 Bivariate Classical Model.
11.2 Multivariate Classical Model.
12 Statistical Graphics.
Bibliography
Cerioli A. Zani S. Analisi dei dati e data mining per le decisioni aziendali. Giuffrè editore. Milano. 2007
Teaching methods
Classes with applications in Excell.
Assessment methods and criteria
Written and Oral Exams.
Other information
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